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Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case Study

Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case... Hindawi Journal of Advanced Transportation Volume 2018, Article ID 5262514, 12 pages https://doi.org/10.1155/2018/5262514 Research Article Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case Study Chao Li , Mohammad Karimi, and Ciprian Alecsandru Department of Building, Civil and Environmental Engineering, Concordia University, Montrea ´ l, QC, Canada H3G 2W1 Correspondence should be addressed to Chao Li; chaoli0351@gmail.com Received 2 June 2017; Revised 18 January 2018; Accepted 4 March 2018; Published 5 April 2018 Academic Editor: Alain Lambert Copyright © 2018 Chao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study proposes two general alternative designs to enhance the operation and safety of High Occupancy Vehicle (HOV) lanes at junctions with bus terminals or parking lots. A series of analysis tools, including microscopic simulation, video-based vehicle tracking technique, and Surrogate Safety Assessment Model (SSAM), are applied to model and test the safety and operational efficiency of an HOV road segment near a bus terminal in Qu eb ´ ec as a case study. A metaheuristic optimization algorithm (i.e., Whale Optimization Algorithm) is employed to calibrate the microscopic model while deviation from the observed headway distribution is considered as a cost function. eTh results indicate that this type of HOV configurations exhibits significant safety problems (high number of crossing conflicts) and operational issues (high value of total delay) due to the terminal-bound buses that frequently need to travel across the main road. It is shown that the proposed alternative geometry design efficiently ameliorates the traffic conflicts issues. In addition, the alternative control design scheme significantly reduces the public transit delay. It is expected that this methodology can be applied to other reserved lane configurations similar to the investigated case study. 1. Introduction Currently, efforts are continually being made to explore the new ways to improve the operation and safety of HOV eTh HOV lane represents a restricted usage traffic lane facilities. However, there is no universally accepted method reserved for vehicles carrying a predetermined number of to evaluate the effectiveness of safety of certain HOV facilities occupants. eTh implementation of an HOV lane system tar- [4]. Some studies focused on the HOV safety evaluation gets mobility improvement of both current and future road- based on the statistical analysis of accidents data during way networks. Considering over forty years of deployment of long periods [5]. Several studies examined the safety of HOV lanes, it has been proven that reserved lanes contribute HOV facilities with respect to different types of geometric to mitigating traffic congestion in urban areas and reduce the design based on the collision and driving behavior (i.e., person-hour delay eeff ctively [1, 2]. However, many problems lane-changing) data [6, 7]. Nevertheless, obtaining reliable related to various implementations of HOV lanes have been accident data over a long enough period is not always identified. These problems can be roughly classified into two possible, especially for recently deployed facilities. A reliable categories, the reduction of capacity (for the non-HOV users) accident-based analysis takes a long time to establish and thus and potential tracffi safety issues, respectively. The former is not suitable for current urban traffic system development. category may include increased congestion on the adjacent In addition, many characteristics of the urban traffic system General Purpose (GP) lanes and/or reduction of vehicle may change over time (e.g., traffic demand volumes, road speeds due to the merging maneuvers of High Occupancy alignments, traffic mix, etc.), and this might require an Vehicles into the GP lanes. eTh latter category is mainly expedited method to assess the existing traffic conditions. related to the lane changes at prohibited locations, especially in the proximity of junctions with other road facilities, such Accordingly, using conflict analysis as a method of safety as bus terminals or parking lots [3]. assessment is preferable, as it makes analyzing the safety 2 Journal of Advanced Transportation improvement before implementing any treatment in the real was developed over the past couple of decades. This approach world possible. was possible mainly due to the advancements in computing However, the geometric configuration of an HOV facility technologies that allowed the development of enhanced has signicfi ant impacts on the safety performance [7]. For tracs ffi imulationmodelstobeabletoreplicate vehicleinter- instance, conducting the before-aer ft study of converting the actions through modeling complex driving behaviors [16, 17]. continuous access to limited access of lane changes in HOV A signicfi ant advantage of simulation-based safety analysis is lanes has shown a significant decrease in conflict occurrence. that microsimulation models can easily generate and measure eTh refore, the HOV facilities with limited access are expected various safety performance indicators [18, 19]. The typical to be safer than those with continuous access. To validate this safety performance indicator is the vehicular conflict, given conclusion, more studies must be conducted. However, there that conflicts can be observed more frequently than crashes is limited opportunity for researchers to conduct before- and that their frequency is expected to be correlated with the aer ft studies of road facilities with respect to the geometric crash occurrence [14, 20–22]. Various studies have validated modicfi ation, because they are too infrequent. eTh refore, the statistical significance and correlation between conflicts utilizing simulation tools may be an effective remedial and accidents [23–26]. measure to overcome the limitation of data availability and A dedicated tool, namely, SSAM was developed by to evaluate the impacts of potential geometric alignment Federal Highway Administration (FHWA) to automatically changes of existing facilities. Several studies have introduced identify,classify, andevaluatetheseverityof thesimulated the evaluation of safety or capacity of HOV facilities utilizing traffic conflicts [14]. Several studies showed that by com- microsimulation [3, 8, 9]. However, these studies mainly bining VISSIM and SSAM a reliable tool for traffic safety focused on the analysis results of the study areas. er Th efore, evaluation can be used, provided that a consistency between it is necessary to develop a systematic assessment method for thefieldobservedandsimulatedconflictsisobserved[27,28]. HOV lanes. In particular, the HOV deployment on arterials Another study proposed a two-step calibration procedure in the proximity of the terminals and parking lots can be of VISSIM (Wiedemann model) to enhance correlation conducted using real-word data to calibrate a microscopic between simulated and efi ld-measured conflicts [29]. eTh re- simulation model. fore, if the simulation model is properly calibrated, it can be In this study, a VISSIM microsimulation model is devel- used to represent reliably the real-world traffic network in oped to test the safety and operational efficiency of an urban terms of both operation and safety parameters. HOV facility near a bus terminal in Queb ´ ec, Canada. This model is calibrated by employing a metaheuristic optimiza- 3. Methodology tion algorithm–Whale Optimization Algorithm (WOA)—to minimize the deviation of simulations results from the 3.1. Modeling of Geometry and Flow. Typically, more detailed observed data. Two general alternative network designs are information contained in the simulation model contributes proposed for comparison analysis (i.e., one modifies the to capturing more reliably the traffic conditions at a given existing road geometric alignment; another one proposes a study area. This is especially important for a traffic safety change in the existing traffic control strategy). To assess the simulation model, which requires good accuracy of both road safety impact of the proposed alternative designs, the simulated capacity and vehicle performance. Surrogate Safety Assessment Model (SSAM) is applied to The basic input to this model is represented by the road compare the simulated vehicle conflicts between the existing characteristics (i.e., the number of lanes on each direction, network and the alternative solutions. The results indicate the lane separation type, and the position of access). In this that the status quo of the study area exhibits a safety problem study, thelinks andconnectorsofthestudyareawerebuilt due to high interactions between buses and passenger cars. in VISSIM by means of an aerial photo from Google Maps. The proposed alternative geometry design efficiently elimi- Some details of the geometry, for example, the access position nates the traffic conflict. In addition, the alternative control ofthepublictransit terminal,weremeasuredonthefield design scheme significantly reduces the public transit delay. and were compared with the efi ld-recorded videos to ensure the accuracy. Similarly, the position of the reserved lane was collected on the field and included in the simulation model. 2. Literature Review Traffic flow is another important input parameter as it Traditionally, most traffic safety studies employed statistical relates to the road capacity, one of the potential calibration analysis of accident records within a given study area [10–13]. variables. Traffic flows were measured using the videos Several studies pointed out the drawbacks of using authority recorded at the study area—the following data was collected: reported crash data for safety evaluation, for example, the the vehicle counts of each lane, vehicle routes within the study lack of ability to evaluate the safety of traffic facilities yet area,and thevehicletypes(e.g.,bus,truck,and passenger to be built or to assess the tracffi remediation solution yet cars). In this study, in order to smooth out random variations to be applied in the field. In addition, the seldom and in flows, while maintaining good precision, the vehicle flows random occurrence of traffic accidents lead to the slowness were recorded and input into the model in five-minute of establishing analysis [14] or the lack of ability to deduce increments. An additional vfi e-minute period without vehicle the crash process [15, 16]. On account of these drawbacks, demand was included at the end of each simulation scenario an alternative safety evaluation approach which includes the to avoid truncating the analysis period observed in the field. computer microsimulation modeling of vehicle interactions To model the observed vehicle composition, road users were Journal of Advanced Transportation 3 identified and classified into three categories, passenger cars, the field. The gap time needed for crossing at the conflict area buses, and trucks, respectively. eTh basic vehicle character- was determined similarly by reviewing the video recordings. istics, for example, the acceleration rate, vehicle length, and Another important VISSIM calibration parameter is the vehicle weight of each vehicle type, can be modeled separately avoid blocking value, which defines the ratio of vehicles that in VISSIM so as to reflect the traffic more realistically. To do not stop in the middle of a junction. This value is defaulted determine individual vehicle routes, vehicles were tracked in to be 100% in VISSIM; in other words, all the vehicles will from the videos generated by three cameras that were used follow the rule, not to block the junctions, if there is stopping tocoverthe wholestudyarea.Theroute of eachvehiclein traca ffi head.However,byreviewing thevideorecorded atthe the simulation was assigned in strict accordance with the study area, no vehicle obeyed this rule. eTh refore, to reflect path observed on the video recordings to ensure a realistic the real conditions, this value is set to 0% for all the conflict representation of the study area. areas in simulation models used in this study. 3.2. Modeling of Traffic Signal. The peak hour tracffi signal 3.4. Modeling of Driving Behavior. Properly modeling of the cycle length and the red, amber, and green time intervals field observed driving behavior is critical for road safety on each direction were collected on the eld fi and modeled evaluation, since it directly influences the vehicle interactions in a micro level. Microsimulation tool VISSIM adopted in VISSIM. In this study, a fix-cycled signal program was built and set at the intersection to replicate the traffic light Wiedemann car following model as the main portion for modeling the vehicle longitudinal movement and rule-based at the study area. Some additional signal control strategy was laws for modeling of vehicle lateral movement and lane used in this study to improve the network performance; for change behavior. example, a fix signal cycle contains a protected left-turn phase In this study, the Wiedemann 74 model is selected to at the intersection and a pulse-triggered signal at the public simulate the urban motorized tracffi as suggested by the VIS- transit terminal. SIM user’s manual [30]. This model contains three adjustable To improve the efficiency of public transit, a pulse- parameters, respectively, the average standstill distance,the triggered signal control was implemented by adding a detec- additive part of safety distance,and multiplicative part of toratthe exitofthe terminal andsignalheads linked with safety distance. Average standstill distance defines the average the detector near the terminal. An add-on signals design desired distance between two cars. Additive part of safety model, namely, Vehicle Actuated Programming (VAP) was distance and multiplicative part of safety distance represent the programmed to control this actuated signal. Typically, a values used for the computation of the desired safety distance. signal phaseofpermanent greenonthemainstreetand For the initial simulation, the values of these three parameters permanentredontheminor road istoggledwhennobuses are usually defined with the default value. However, they must are detected. Meanwhile, when the existing buses are detected be calibrated later to suite the real driving behaviors of the by the sensor, the signal is programmed to switch to the study site. complementary phase (i.e., green signal on the minor road The lane change behaviors are defined by a rule-based andredon themainroad),thusprotectingthemovementsof model in VISSIM. In this model, the critical parameter that buses crossing through multiple lanes. decides whether a lane change would be executed is the minimum headway. Avehicle canonlychangelanewhen 3.3. Modeling of Right of Way without Signal Control. In there is a distance gap arrival at the adjacent lane that is bigger VISSIM priority rules are defined to capture the conflicting than the predetermined minimum headway. Otherwise, it tracflffi owsthatarenotcontrolledbysignals.Inthisstudy, hastoeithertravelcontinuouslyorstopandwaituntil the priority rules were set at the entry and the exit zones of the occurrence of an enough gap for it in order to merge thebus terminal,inorder to realisticallymodeltheaccess accordingtoitspredenfi edroute.Inthisstudy,thevalueofthe and egress movements of buses as they were observed in the minimum headway was determined by reviewing the videos. video recordings. Typically, the buses travel to and from the Another noticeable parameter defined in the lane change terminal, yielding to the vehicles traveling along the main model is the advanced merging;thisoptionisselectedin arterial, and stop in position near the access or exit until this study; thus more vehicles can change lanes earlier when acceptable gaps occur on both directions on the main road. following their routes, as encountered in the videos. Two thresholds are set for the priority rules to conne fi the crossing of the yielding vehicles, respectively, the minimum 3.5. Measurement of Vehicle Speed Distribution by Feature- headway and the minimum gap time. A yielding vehicle will Based Tracking. Vehicle speed distribution is an important stop before the stop line until both predetermined thresholds input parameter for safety simulation. While potentially more are achieved. The values of the thresholds are determined by accurate, individual vehicle speed on multiple lanes is usually reviewing all the accepted gaps and headways by the crossing dicffi ult to measure on the efi ld simultaneously with radar buses from the video. devices. Therefore, an alternative method was applied in this The conflict areas are automatically generated in VISSIM study to measure the vehicle speed, which is the video-based where links or connectors overlap. In this study, the priority feature tracking. rules at the conflict areas were set to capture the vehicles An open-sourced software project, namely, Traffic Intel- approaching the conflict area from the minor road and ligence, was used to automatically track and measure the yielding those from the main road, as typically observed in speed of thevehiclescaughtbythe videoatthe study 4 Journal of Advanced Transportation Figure 2: Feature tracking process by Tracffi Intelligence . Figure 1: Points selected on the video frame to compute homogra- phy file. site [31]. Traffic Intelligence consists of a set of tools that work cooperatively for tracffi data processing and analysis, including camera image calibration, feature tracking, and trajectory data analysis. 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 The feature-based tracking algorithm utilizes a homogra- Time headway range (s) phy file that projects the camera image space to the real-world Figure 3: Observed vehicle gap distribution. ground plane. The homography file was created by utilizing a video frame and a corresponding aerial photo with known scale (pixels per meter). In this study, an aerial photo of the studysitefromGoogleMapswithknownscaleof0.21pixels model,becausethe vehicletimegapdirectly reflectsthe per meter was adopted. In total ten noncollinear visible points car following behavior. eTh real vehicle gaps were observed on thevideo framewerepositionedonthe aerialphoto;thus, manually from the video using the MPC player that provides the video image was projected to the aerial photo, and the milliseconds accuracy. Because the vehicles travel westbound vehicles tracked in the video were deemed to be tracked in the pass through a signalized intersection before they enter the real-world plane with their speeds. Figure 1 shows the points cameras field of view, to eliminate the impact of the red time projected to the aerial photo from the video frame. at the intersection, the time gaps bigger than 5 seconds were Based on the computed homography file, the feature ignored. The distribution of all the observed gaps that are tracking program can be run. The predetermined number of smallerorequalto 5seconds wasrecordedinahistogram features of each vehicle in the video was detected and tracked with a sample rate of 0.3 seconds. Figure 3 shows the observed frame by frame until the vehicle leaves the video capture vehicle gap distribution. area. In order to suppress the interference of the shadows, a In this paper, the Whale Optimization Algorithm (WOA), mask imagewascreatedand toggledwiththe videoimage; a metaheuristic nature-based algorithm, is applied to cali- therefore only the features within the white range of the brate the model. The deviation of the simulated headway mask image can be detected, and the shadows can be filtered distribution from its’ observed distribution is considered as out. eTh features that move consistently were then grouped the objective function to be minimized during the calibration together to generate the trajectory file of each vehicle, and all process. WOA is inspired by hunting behavior of humpback the trajectories generated from the video were written into a whales. It is defined as “the simulated hunting behavior with database. eTh average speed of each vehicle can be easily read random or the best search agent to chase the prey and the by processing their trajectories. Figure 2 shows the feature use of a spiral to simulate bubble-net attacking mechanism tracking process by Traffic Intelligence . of humpback whales” [32]. eTh hunting behavior of whales is representative of the procedure of this algorithm. 3.6. Model Calibration. In order to determine the optimum The three parameters of the Wiedemann 74 model in values for the calibration parameters, an objective function VISSIM (i.e., average standstill distance (ASSD), additive should be defined based on the error between observed data part of safety distance (APSD), and multiplicative part of andsimulated data.Theobjectivefunction isthedeviation safety distance (MPSD)) which has the highest impact on the of the simulated gap distribution from the real observed modelhavebeenselectedtobecalibrated.UsingMATLAB, gap distribution. In order to test this goodness of tfi (objec- an optimization toolbox connecting to COM-interface of tive), the Chi-square test was employed. In this study, the VISSIM by M-file programming in MATLAB, the calibration westbound vehicle gap distribution on the GP lane near process has been accomplished. Aer ft 190 simulation runs, the bus terminal was taken as the criterion to calibrate the the optimal values of parameters were determined to be as Frequency Journal of Advanced Transportation 5 follows: ASSD = 1.156, APSD = 0.637, and MPSD = 8.079. lane changing, and rear ending, respectively. eTh thresholds of For diverse random seeds, the simulation results showed the conflict angles were adjusted to 2 degree and 45 degree as these optimal parameters lead to statistically matching the suggested by previous studies [8]. Basically, detected conflict observed headway distributions with the simulated ones at which has a conflict angle of 2 degrees or less is defined as 90% confidence level. It is worth mentioning that the simple rear ending conflict; if the conflict angle is between 2 and 45 way to optimize the cost function is exploring the whole degrees, it is detected as lane changing; while if the conflict possible region of the parameters to n fi d the global minimum, angle is bigger than 45 degrees, it is recorded as the crossing which is extremely time-consuming. For this case study, these type. However, due to the peculiarity of geometry of each optimization parameters took values within the following study area, the link information of all the output conflicts, intervals: ASSD between 0 and 2, APSD between 0 and 1, and which was also detected by SSAM, was manually checked to MPSD between 0 and 10. By exploring these intervals, the properly determine their type. The three types of conflicts optimal values were found after nearly 1000 simulation runs. were recorded for subsequent comparative safety analysis. The lateral movement of buses that merge into the main A built-in filter of SSAM can be applied to screen tracffi from HOV lane or travel across the road when an out the conflicts caused by each measured movement by acceptable gap was identified was also calibrated by adjusting reading the corresponding link information. eTh spots where the parameters of the priority rule. eTh minimum gap time conflicts were detected can be plotted automatically on the and distance headway were set to 6 seconds and 20 meters, togglednetwork imagebypositioning theVISSIMnetwork respectively, similar to the values observed in the recorded coordinates. The conflicts of different types can be showed in videos. It is noticeable that a part of the terminal-bound different shapes or colors on the togged map to give a visual buses changed lanes between the reserved HOV lane and estimate of the hotspot areas (i.e., conflicts’ frequency and theadjacentGPlanebeforethe intersection;thisbehavioris density). reflected in the simulation model. 3.9. Summary. The methodology presented in this study 3.7. Simulation Output. VISSIM provides direct output of introduces a simulation-based approach to evaluate road various kinds of simulation results. In this study, the vehicle network safety and efficiency. To apply this methodology, the delay and trajectory were analyzed to evaluate the operational efi ld traffic conditions are collected, and the detailed informa- efficiency and safety of the study area. tion including the efi ld geometry, control strategy, flow, and Vehicledelaydata canbegeneratedby setting vehicle driving behavior is reviewed. Such basic information is then travel time on the defined vehicle routes, which are defined integrated in a VISSIM simulation model. With an important by a Starting Point and an End Point,respectively. Forthe model parameter, the vehicle speed distributions are obtained vehicles that pass through the Starting Point and the End using a feature tracking program, namely, Traffic Intelligence . Point, successively, the travel time delays are automatically The model is properly calibrated until the output vehicle calculated. eTh vehicle delays of the interested vehicle routes time gap distribution compared well with the efi ld observed were then analyzed to evaluate the operational ecffi iency of vehicle gap distribution by applying the Chi-square test. the network. The model output vehicle delays are reviewed for network The trajectories of all the simulated vehicles can be operational efficiency analysis, and the model output vehicle generated by VISSIM, and the recorded trajectory data was trajectory files are analyzed by SSAM to determine the then analyzed using SSAM, to evaluate the vehicle conflicts conflict within the study area thus giving the safety level of within the network. For each simulation run, dieff rent sim- the site. Figure 4 shows the flow chart of the methodology ulation random seeds were applied, and the output results used in this study for traffic safety and operational efficiency were averaged for analysis purposes. This simulation setup evaluation. scenario accounts for the stochastic properties of the simu- lation model, thus reflecting real-world traffic behavior more 4. Case Study realistically. 4.1. Study Area Description. es Th tudyareausedinthis 3.8. Analyzing Vehicle Conicts fl Using SSAM. The vehicle studyisasegmentofRte-116, asuburbanhighwayinLevis, ´ trajectory data collected from VISSIM was used in SSAM Queb ´ ec. Evaluations of traffic safety and operations were to assess the vehicle conflicts detected in the study area. made at a specific location along the four-lane east-west Most studies evaluate tracffi safety through two surrogate arterialsegmentthatincludesoneGPlaneandoneHOVlane, measures, Time to Collision (TTC) and Postencroachment in both directions. eTh eastbound reserved lane allows buses Time (PET). Values below a commonly accepted threshold and passenger cars with three or more passengers, while the of either TTC or PET value indicates a higher probability of westbound direction has a bus-only lane. eTh current design collision. SSAM is able to automatically estimate the TTC and of this facility is such that the westbound buses arriving at or PET values of each vehicle interaction and thus to record all departing from the terminal have to travel across the four- potential conflicts. In this study, the TTC and PET were set to lane undivided road. Figure 5 shows the current paths of the 1.5 seconds and 5 seconds, respectively, the values frequently buses using the terminal. established by previous research studies [20, 33]. The traffic video feeds of vehicles accessing the terminal, The detected conflicts were classified into three types, the commuter parking lot, and traveling along Rte-116 were based on the predetermined conflict angles, namely, crossing, collectedvia GoProHDvideo camerasthatwereinstalled N Bus Bus Bu Bu Bu Bu B B B B B Bu u u u u u u u u us us us us us us us us r re es se erv rv rv rv rv rv rv rv rv rv rv rv rv rv rv v v ve e e e e e e e e e e e e e e e e e e ed l d d d d d d d d d d d d d d d d d d l l l l l l l l l l l l l l l l l l l l l l l l l l l l lan an ane an an an an an an a a a a a a a a an n n n n n n n n n n n n ne e 6 Journal of Advanced Transportation Data collection Basic simulation Speed distribution Observed gap parameters (traffic intelligence) distribution Speed distribution (traffic Intelligence) Model parameter adjusting using optimization method Simulated vehicle gap Pass Chi- No square test Yes Vehicle delay output Vehicle trajectory output Conflict analysis (SSAM) Evaluation results Figure 4: Framework of evaluation procedure. on top of extendable masts along the roadway. Cameras 1 and 2 were both installed at the same location with views opposing each other. The orientations of these two cameras were adjusted to capture east-west traffic that interacts with both access points into and out of the bus terminal. Camera 3 was installed at the proximity of the commuter parking lot entry/exit gate, to capture interactions between main road traffic and vehicles to and from the parking. eTh positions of the cameras are shown in Figure 5. eTh video tracffi data of the PM peak hour (4:30 pm∼5:30 pm) was used in the final analysis of this study. Figure 5: Paths of the terminal-bound buses. A probe vehicle was driven several times along the study segments with an arbitrarily selected constant speed. eTh known speed values were used to calibrate the postprocessing speed detection measuring software, Traffic Intelligence .A fixed 88-second cycle of the tracffi signal along Rte-116 at the distinguished into four types: passenger cars (on the GP adjacent intersection (i.e., 40 seconds, red, 40 seconds, green, lane), buses, trucks, and reserved lane users, respectively. and 4 seconds, yellow) was measured in the eld fi and used in This den fi ition of the tracffi mix was necessary to capture the simulation model of the study area. more reliably the vehicle interactions in the traffic simulation The video files from each camera were processed in 5- model (different vehicle types exhibit different driving behav- minute increments to manually determine the distribution iors in terms of acceleration, minimum headway, etc.). Tables of tracflffi owsduringthe analysis period.Vehicleswere 1 and 2 show a classification of westbound and eastbound Cam 3 Cam 2 Cam 1 Journal of Advanced Transportation 7 Table 1: Observed traffic flow during the peak hour (4:30 pm ∼5:30 pm). Average vehicle flows (vehicles/hour) Time Westbound Eastbound Car Bus Truck HOV Car Bus Truck HOV 4:30 pm∼5:30 pm 663 16 3 7 338 5 4 36 Table 2: Access and egress vehicles during the peak hour (4:30 pm∼5:30 pm). Average vehicle flows (vehicles/hour) Westbound Eastbound Time Bus Parking car Bus Parking car Access Egress Access Egress Access Egress Access Egress 4:30 pm∼5:30 pm 10 14 0 37 7 3 4 18 Table 3: Vehicles characteristics. 2 2 Vehicle type Length (meter) Width (meter) Weight (ton) Maximum acceleration (m/s ) Maximum deceleration (m/s ) Car and HOV 3.75–4.76 1.85–2.07 - 3.5 −7.5 Bus 11.54 3.17 4–12 1.24 −7.5 Truck 13.94 2.63 2.8–40 2.5 −5.5 tracflffi owsalong thehighway,aswellasaccess/egress ofthe buses using the terminal during the aer ft noon peak period. Traffic Intelligence was utilized to measure the vehicle speed. Calibration of the video analysis software was per- formed using various mask pictures to filter the shadows of Bus the moving vehicles until the measured speeds of the probe terminal Parking vehicle were identical to the observed values. eTh vehicle lot speed distributions of both westbound and eastbound vehi- cles were recorded every ve fi minutes and used as simulation input parameters. Figure 6: The status quo network modeled in VISSIM. 4.2. Modeling Existing Configuration and Traffic Conditions (Status Quo). The peak hour tracffi was modeled in VISSIM to evaluate traffic safety and operations of the observed from the current values. Figure 6 represents a snapshot of arterial segment. Vehicle modals used in the simulation are the VISSIM simulation model using the existing geometric selected by VISSIM automatically. The vehicle characteristics alignment and tracffi operations conditions. of thecasestudy areshownin Table3.Toaccountfor the effects of stochastic variation of the model’s parameters, ten different simulations with different random seeds were ran, 4.3. Simulations of Alternative Geometry/Control Designs. andtheaveragevalueswereusedintheanalysis. The main concern related to traffic safety at the investigated The average vehicle delay (excluding signal waiting time study area pertains to the placement of the reserved lanes on at intersection) was measured for three types of movements, the outside lanes. This configuration leads to multiple lanes using the vehicle travel time measurements tool. Movement 1 crossing when left turns are needed and high occurrence of identifies the westbound traffic on the GP lane. Movement 2 is vehicle interactions was observed especially during congested associated with westbound buses entering the terminal (i.e., tracffi conditions. busesmerging from HOVlaneintothe GP laneandthen Two alternative designs have been tested to evaluate crossing the two eastbound lanes). Movement 3 represents their potential to mitigate traffic safety and operations issues. westbound buses leaving the terminal (i.e., buses that cross Figure 7 shows the VISSIM network layout of the first all the four lanes to enter the highway). Vehicle trajectory files alternative design. In this model, westbound buses were were also generated for conflict analysis. prohibited to enter or exit the terminal by crossing the In addition, to evaluate the impact of expected increase highway directly. Instead, an adjacent roadway segment was in tracffi flow on tracffi operations (i.e., average vehicle delay) inserted along the south side of bus terminal, which is directly and safety (i.e., conflicts frequency), the same simulation connected to the minor road. To serve the terminal-bound model was used to evaluate similar scenarios, assuming the buses, ten seconds of left-turning signal phase was provided tracffi volume increases in the future by 10%, 20%, and 30% at the intersection on the main road. Similarly, for each 8 Journal of Advanced Transportation Bus Bus terminal Parking terminal lot Rear end conflict Lane change conflict Crossing conflict Figure 9: Conflicts near bus terminal plots on original network. Figure 7: VISSIM network of alternative road geometry design. Bus terminal Parking lot Movement 1 Movement 2 (buses Movement 3 (buses (westbound roadway into terminal) out of terminal) Figure 8: VISSIM network of alternative control design. traffic) Observed volume Volume increase by 20% Volume increase by 10% Volume increase by 30% traffic demand alternative (i.e., current status, 10%, 20%, and Figure 10: Effects of increasing traffic flow on average delay per 30% increments of vehicular tracffi volume), the collected vehicle. peak hour vehicle flows and speed distributions were used to model the network using ten simulation random seeds. The individual vehicle trajectories and delay measurements of the study area for safety analysis. A built-in filter of SSAM was same movements evaluated for the status quo configuration appliedtoscreenout theconflictscausedbyeachmeasured were collected and used for comparison analysis. Figure 8 movement by reading the corresponding link information. shows the VISSIM network layout of the second alternative design. In this model, a loop detector that controls a signal The spots where conflicts were detected were plotted auto- maticallyonthetogglednetwork imagebyutilizing the set was added to the existing network. This system was used VISSIM network coordinates. eTh conflicts of different types to control the egress of westbound buses as they leave the terminal. eTh add-on signal control model VAP was created were showed in different shapes on the togged map. Figure 9 shows the spatial distribution of conflicts caused by measured to program the signal timing. eTh detector was placed near movements near the bus terminal plotted on the original the exit of the bus terminal. As long as buses are not in the proximity of the sensor, the signal indicates green for network. the main road to allow east-west traffic and red for the bus 4.5. Comparison Analysis of Safety and Operation. Figures 10 exit to prevent the egress buses from traveling across the and 11 represent the impact of different traffic volumes on road directly. When buses are detected at the terminal, exit traffic operations (delay) and safety (conflicts). signal turns green for them and red for traffic on the main road, which allows for protected turns. eTh red signal on the As intuitively expected, more tracffi demand leads to main road lasts for 10 seconds from the last bus detected and increased average delay. It also shows that of the three types then turns back to green until the next detection. eTh same of vehicle interactions analyzed movements labeled 2 and 3 vehicle hourly flows previously processed were used in this (i.e., associated with buses entering and leaving the terminal) simulation scenario, and the same ten different simulation are aeff cted by significantly higher delay than the vehicles random seeds were applied. eTh delay measurements of the moving along the east-west roadway. This is explained by the same types of movements and trajectory data were collected fact that buses have to make left turns from/into the arterial, for comparative analysis. and consequently they do not have the default right of way. In addition, tracffi safety analysis (i.e., evaluation of vehicular 4.4. Surrogate Safety Measures of Vehicle Conicts. fl SSAM interactions through the SSAM tool) shows that, for all levels was applied to assess the vehicle conflicts detected in the of traffic demand, the majority (more than 85%) of vehicular Delay (s/veh) Journal of Advanced Transportation 9 Observed volume Volume increase by 10% Movement 1 (westbound roadway traffic) Movement 1 (westbound roadway traffic) Movement 2 (buses into terminal) Movement 2 (buses into terminal) Movement 3 (buses out of terminal) Movement 3 (buses out of terminal) Volume increase by 20% Volume increase by 30% Movement 1 (westbound roadway traffic) Movement 1 (westbound roadway traffic) Movement 2 (buses into terminal) Movement 2 (buses into terminal) Movement 3 (buses out of terminal) Movement 3 (buses out of terminal) Figure 11: Sensitivity analysis of conflicts distribution (current configuration). conflicts were crossing conflicts associated with the same The sensitivity analysis demonstrates that traffic operations movements of buses that enter or leave the terminal facility. are not impacted by this design. It can be seen that there Moreover, lane-changing conflicts were observed between is a minor positive effect on the average vehicular delay for buses moving from the reserved lane into the GP lane to movement 1 (vehicles traveling westbound on Rte-116), but engage in le-ft turning maneuvers towards the terminal. there is a significant positive effect on the average delay of Figure 12 shows the effects of different traffic volumes on buses accessing the terminal (i.e., a reduction in delay of tracffi operations (magnitude of delay) and safety (frequency about 85%). However, this alternative scenario brings a trade- of conflicts) when the first alternative scenario was used. As off for the movements of buses exiting the terminal that are expected, by including a separation median between the two hindered for most traffic flow levels. The additional delay directions of trac, ffi all vehicular conflicts associated with left- encountered by buses leaving the terminal is due to the fact turn movements into and out of the terminal are eliminated. that, for this design, the westbound egress buses must use 10 Journal of Advanced Transportation −20 −40 −60 −80 −100 Movement 1 Movement 2 Movement 3 (westbound (buses into (buses out of roadway traffic) terminal) terminal) Observed volume −6% −85% 69% Volume increase by 10% −9% −85% 40% Volume increase by 20% −9% −85% 1% Volume increase by 30% −12% −87% −18% Figure 12: Effects of first alternative design on the average delay (separation median). −20 −40 −60 −80 Movement 1 Movement 2 Movement 3 (westbound (buses into (buses out of roadway traffic) terminal) terminal) Observed volume 16% −18% −49% −31% Volume increase by 10% 16% −62% −34% Volume increase by 20% 17% −71% 17% −47% −73% Volume increase by 30% Figure 13: Effects of second alternative design on the average delay (traffic control). the nearby intersection, and the traffic signal timing was different, due to breaking at red light; it is expected that rear not optimized to accommodate left-turning buses from the ending conflicts might be more severe. On the other hand, minor street. rerouting buses through the intersection via the minor street eTh results for the second alternative design (i.e., control- seems to be the best option, because it eliminates completely ling the access/egress of buses for Movements 2 and 3 via all conflicts of le-ft turning vehicles while its impact on a bus-triggered traffic control signal, in order to reduce the traffic operations might not be significant, since it can be vehicle interactions with the buses) are shown in Figure 13. It mitigated with optimizing the traffic signal timing plan at the can be seen that this alternative design reduces considerably intersection. thedelay of busesinand outoftheterminal (Movements2 To conclude, the existing geometric and traffic signal and 3), while it increases by less than 17% the delay of vehicles configurations show that there is a high occurrence of traveling westbound along the arterial (Movement 1). vehicular conflicts for left-turn buses approaching terminal. More importantly, the vehicular conflict analysis of these It canbeseenfromthe resultsabovethat,byusing the results shows the elimination of the crossing conflicts (Move- alternative designs, these types of conflicts are eliminated. ments 2 and 3) related to buses accessing/leaving the terminal In addition, the proposed alterations to existing alignment by turning left across the HOV and GP lanes. In addition, provide benefits for traffic operations because they reduce this design has no impact on the low conflict occurrence of significantly the average vehicular delay. However, when Movement 1 (vehicles moving westbound on the arterial). traffic signals are used to control for protected le-ft turn Several aspects of the proposed alternative designs are buses that are rerouted through the adjacent intersection, discussedattheendofthissection.Thedelay of thetracffi an additional analysis of signal delay and optimization is flow moving westbound on the arterial during the peak necessary. period was compared across all three simulation scenarios Similarly, the analysis of the measured movement 3 (i.e., (i.e., current design, separation barrier, and traffic control westbound buses leaving from terminal) identifies a large alternative). It was found that the tracffi control alternative number of crossing conflicts within the east-west traffic leads to the most negative impact on the vehicular delay. on the main arterial. Elimination of these conflicts can be In addition, conflict occurrence between the current design achieved if this movement is protected either through the and the proposed traffic control design is not signicfi antly tracffi signal sensitive to the buses present at the terminal exit, Relative change of Relative change of Average vehicular average vehicular delay (%) delay (%) Journal of Advanced Transportation 11 or by using the barrier separated geometry that reroutes the Acknowledgments buses via the adjacent intersection. The results indicate that Thisstudy wasfundedbyMinist er ` e des Transports du the network with alternative control design is the best for Que´bec (MTQ) through research Project R706.1. eTh authors departing buses (i.e., the delay is the smallest). wouldliketothank MatinGiahiFoomaniandGiaHungLieu As expected, the sensitivity analysis shows that an from Concordia University for the help in data collection. increased main arterial traffic volume leads to negative effects on the conflict frequency and average vehicular delay, References regardless of the design used, while the alternative designs provide elimination or significant reduction in conflicts. [1] C. Fuhs and J. Obenberger, “Development of high-occupancy vehicle facilities: Review of national trends,” Transportation 5. Concluding Remarks Research Record, no. 1781, pp. 1–9, 2002. [2] M. Menendez and C. F. 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Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case Study

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Hindawi Publishing Corporation
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Copyright © 2018 Chao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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0197-6729
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2042-3195
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10.1155/2018/5262514
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Hindawi Journal of Advanced Transportation Volume 2018, Article ID 5262514, 12 pages https://doi.org/10.1155/2018/5262514 Research Article Microscopic Simulation-Based High Occupancy Vehicle Lane Safety and Operation Assessment: A Case Study Chao Li , Mohammad Karimi, and Ciprian Alecsandru Department of Building, Civil and Environmental Engineering, Concordia University, Montrea ´ l, QC, Canada H3G 2W1 Correspondence should be addressed to Chao Li; chaoli0351@gmail.com Received 2 June 2017; Revised 18 January 2018; Accepted 4 March 2018; Published 5 April 2018 Academic Editor: Alain Lambert Copyright © 2018 Chao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study proposes two general alternative designs to enhance the operation and safety of High Occupancy Vehicle (HOV) lanes at junctions with bus terminals or parking lots. A series of analysis tools, including microscopic simulation, video-based vehicle tracking technique, and Surrogate Safety Assessment Model (SSAM), are applied to model and test the safety and operational efficiency of an HOV road segment near a bus terminal in Qu eb ´ ec as a case study. A metaheuristic optimization algorithm (i.e., Whale Optimization Algorithm) is employed to calibrate the microscopic model while deviation from the observed headway distribution is considered as a cost function. eTh results indicate that this type of HOV configurations exhibits significant safety problems (high number of crossing conflicts) and operational issues (high value of total delay) due to the terminal-bound buses that frequently need to travel across the main road. It is shown that the proposed alternative geometry design efficiently ameliorates the traffic conflicts issues. In addition, the alternative control design scheme significantly reduces the public transit delay. It is expected that this methodology can be applied to other reserved lane configurations similar to the investigated case study. 1. Introduction Currently, efforts are continually being made to explore the new ways to improve the operation and safety of HOV eTh HOV lane represents a restricted usage traffic lane facilities. However, there is no universally accepted method reserved for vehicles carrying a predetermined number of to evaluate the effectiveness of safety of certain HOV facilities occupants. eTh implementation of an HOV lane system tar- [4]. Some studies focused on the HOV safety evaluation gets mobility improvement of both current and future road- based on the statistical analysis of accidents data during way networks. Considering over forty years of deployment of long periods [5]. Several studies examined the safety of HOV lanes, it has been proven that reserved lanes contribute HOV facilities with respect to different types of geometric to mitigating traffic congestion in urban areas and reduce the design based on the collision and driving behavior (i.e., person-hour delay eeff ctively [1, 2]. However, many problems lane-changing) data [6, 7]. Nevertheless, obtaining reliable related to various implementations of HOV lanes have been accident data over a long enough period is not always identified. These problems can be roughly classified into two possible, especially for recently deployed facilities. A reliable categories, the reduction of capacity (for the non-HOV users) accident-based analysis takes a long time to establish and thus and potential tracffi safety issues, respectively. The former is not suitable for current urban traffic system development. category may include increased congestion on the adjacent In addition, many characteristics of the urban traffic system General Purpose (GP) lanes and/or reduction of vehicle may change over time (e.g., traffic demand volumes, road speeds due to the merging maneuvers of High Occupancy alignments, traffic mix, etc.), and this might require an Vehicles into the GP lanes. eTh latter category is mainly expedited method to assess the existing traffic conditions. related to the lane changes at prohibited locations, especially in the proximity of junctions with other road facilities, such Accordingly, using conflict analysis as a method of safety as bus terminals or parking lots [3]. assessment is preferable, as it makes analyzing the safety 2 Journal of Advanced Transportation improvement before implementing any treatment in the real was developed over the past couple of decades. This approach world possible. was possible mainly due to the advancements in computing However, the geometric configuration of an HOV facility technologies that allowed the development of enhanced has signicfi ant impacts on the safety performance [7]. For tracs ffi imulationmodelstobeabletoreplicate vehicleinter- instance, conducting the before-aer ft study of converting the actions through modeling complex driving behaviors [16, 17]. continuous access to limited access of lane changes in HOV A signicfi ant advantage of simulation-based safety analysis is lanes has shown a significant decrease in conflict occurrence. that microsimulation models can easily generate and measure eTh refore, the HOV facilities with limited access are expected various safety performance indicators [18, 19]. The typical to be safer than those with continuous access. To validate this safety performance indicator is the vehicular conflict, given conclusion, more studies must be conducted. However, there that conflicts can be observed more frequently than crashes is limited opportunity for researchers to conduct before- and that their frequency is expected to be correlated with the aer ft studies of road facilities with respect to the geometric crash occurrence [14, 20–22]. Various studies have validated modicfi ation, because they are too infrequent. eTh refore, the statistical significance and correlation between conflicts utilizing simulation tools may be an effective remedial and accidents [23–26]. measure to overcome the limitation of data availability and A dedicated tool, namely, SSAM was developed by to evaluate the impacts of potential geometric alignment Federal Highway Administration (FHWA) to automatically changes of existing facilities. Several studies have introduced identify,classify, andevaluatetheseverityof thesimulated the evaluation of safety or capacity of HOV facilities utilizing traffic conflicts [14]. Several studies showed that by com- microsimulation [3, 8, 9]. However, these studies mainly bining VISSIM and SSAM a reliable tool for traffic safety focused on the analysis results of the study areas. er Th efore, evaluation can be used, provided that a consistency between it is necessary to develop a systematic assessment method for thefieldobservedandsimulatedconflictsisobserved[27,28]. HOV lanes. In particular, the HOV deployment on arterials Another study proposed a two-step calibration procedure in the proximity of the terminals and parking lots can be of VISSIM (Wiedemann model) to enhance correlation conducted using real-word data to calibrate a microscopic between simulated and efi ld-measured conflicts [29]. eTh re- simulation model. fore, if the simulation model is properly calibrated, it can be In this study, a VISSIM microsimulation model is devel- used to represent reliably the real-world traffic network in oped to test the safety and operational efficiency of an urban terms of both operation and safety parameters. HOV facility near a bus terminal in Queb ´ ec, Canada. This model is calibrated by employing a metaheuristic optimiza- 3. Methodology tion algorithm–Whale Optimization Algorithm (WOA)—to minimize the deviation of simulations results from the 3.1. Modeling of Geometry and Flow. Typically, more detailed observed data. Two general alternative network designs are information contained in the simulation model contributes proposed for comparison analysis (i.e., one modifies the to capturing more reliably the traffic conditions at a given existing road geometric alignment; another one proposes a study area. This is especially important for a traffic safety change in the existing traffic control strategy). To assess the simulation model, which requires good accuracy of both road safety impact of the proposed alternative designs, the simulated capacity and vehicle performance. Surrogate Safety Assessment Model (SSAM) is applied to The basic input to this model is represented by the road compare the simulated vehicle conflicts between the existing characteristics (i.e., the number of lanes on each direction, network and the alternative solutions. The results indicate the lane separation type, and the position of access). In this that the status quo of the study area exhibits a safety problem study, thelinks andconnectorsofthestudyareawerebuilt due to high interactions between buses and passenger cars. in VISSIM by means of an aerial photo from Google Maps. The proposed alternative geometry design efficiently elimi- Some details of the geometry, for example, the access position nates the traffic conflict. In addition, the alternative control ofthepublictransit terminal,weremeasuredonthefield design scheme significantly reduces the public transit delay. and were compared with the efi ld-recorded videos to ensure the accuracy. Similarly, the position of the reserved lane was collected on the field and included in the simulation model. 2. Literature Review Traffic flow is another important input parameter as it Traditionally, most traffic safety studies employed statistical relates to the road capacity, one of the potential calibration analysis of accident records within a given study area [10–13]. variables. Traffic flows were measured using the videos Several studies pointed out the drawbacks of using authority recorded at the study area—the following data was collected: reported crash data for safety evaluation, for example, the the vehicle counts of each lane, vehicle routes within the study lack of ability to evaluate the safety of traffic facilities yet area,and thevehicletypes(e.g.,bus,truck,and passenger to be built or to assess the tracffi remediation solution yet cars). In this study, in order to smooth out random variations to be applied in the field. In addition, the seldom and in flows, while maintaining good precision, the vehicle flows random occurrence of traffic accidents lead to the slowness were recorded and input into the model in five-minute of establishing analysis [14] or the lack of ability to deduce increments. An additional vfi e-minute period without vehicle the crash process [15, 16]. On account of these drawbacks, demand was included at the end of each simulation scenario an alternative safety evaluation approach which includes the to avoid truncating the analysis period observed in the field. computer microsimulation modeling of vehicle interactions To model the observed vehicle composition, road users were Journal of Advanced Transportation 3 identified and classified into three categories, passenger cars, the field. The gap time needed for crossing at the conflict area buses, and trucks, respectively. eTh basic vehicle character- was determined similarly by reviewing the video recordings. istics, for example, the acceleration rate, vehicle length, and Another important VISSIM calibration parameter is the vehicle weight of each vehicle type, can be modeled separately avoid blocking value, which defines the ratio of vehicles that in VISSIM so as to reflect the traffic more realistically. To do not stop in the middle of a junction. This value is defaulted determine individual vehicle routes, vehicles were tracked in to be 100% in VISSIM; in other words, all the vehicles will from the videos generated by three cameras that were used follow the rule, not to block the junctions, if there is stopping tocoverthe wholestudyarea.Theroute of eachvehiclein traca ffi head.However,byreviewing thevideorecorded atthe the simulation was assigned in strict accordance with the study area, no vehicle obeyed this rule. eTh refore, to reflect path observed on the video recordings to ensure a realistic the real conditions, this value is set to 0% for all the conflict representation of the study area. areas in simulation models used in this study. 3.2. Modeling of Traffic Signal. The peak hour tracffi signal 3.4. Modeling of Driving Behavior. Properly modeling of the cycle length and the red, amber, and green time intervals field observed driving behavior is critical for road safety on each direction were collected on the eld fi and modeled evaluation, since it directly influences the vehicle interactions in a micro level. Microsimulation tool VISSIM adopted in VISSIM. In this study, a fix-cycled signal program was built and set at the intersection to replicate the traffic light Wiedemann car following model as the main portion for modeling the vehicle longitudinal movement and rule-based at the study area. Some additional signal control strategy was laws for modeling of vehicle lateral movement and lane used in this study to improve the network performance; for change behavior. example, a fix signal cycle contains a protected left-turn phase In this study, the Wiedemann 74 model is selected to at the intersection and a pulse-triggered signal at the public simulate the urban motorized tracffi as suggested by the VIS- transit terminal. SIM user’s manual [30]. This model contains three adjustable To improve the efficiency of public transit, a pulse- parameters, respectively, the average standstill distance,the triggered signal control was implemented by adding a detec- additive part of safety distance,and multiplicative part of toratthe exitofthe terminal andsignalheads linked with safety distance. Average standstill distance defines the average the detector near the terminal. An add-on signals design desired distance between two cars. Additive part of safety model, namely, Vehicle Actuated Programming (VAP) was distance and multiplicative part of safety distance represent the programmed to control this actuated signal. Typically, a values used for the computation of the desired safety distance. signal phaseofpermanent greenonthemainstreetand For the initial simulation, the values of these three parameters permanentredontheminor road istoggledwhennobuses are usually defined with the default value. However, they must are detected. Meanwhile, when the existing buses are detected be calibrated later to suite the real driving behaviors of the by the sensor, the signal is programmed to switch to the study site. complementary phase (i.e., green signal on the minor road The lane change behaviors are defined by a rule-based andredon themainroad),thusprotectingthemovementsof model in VISSIM. In this model, the critical parameter that buses crossing through multiple lanes. decides whether a lane change would be executed is the minimum headway. Avehicle canonlychangelanewhen 3.3. Modeling of Right of Way without Signal Control. In there is a distance gap arrival at the adjacent lane that is bigger VISSIM priority rules are defined to capture the conflicting than the predetermined minimum headway. Otherwise, it tracflffi owsthatarenotcontrolledbysignals.Inthisstudy, hastoeithertravelcontinuouslyorstopandwaituntil the priority rules were set at the entry and the exit zones of the occurrence of an enough gap for it in order to merge thebus terminal,inorder to realisticallymodeltheaccess accordingtoitspredenfi edroute.Inthisstudy,thevalueofthe and egress movements of buses as they were observed in the minimum headway was determined by reviewing the videos. video recordings. Typically, the buses travel to and from the Another noticeable parameter defined in the lane change terminal, yielding to the vehicles traveling along the main model is the advanced merging;thisoptionisselectedin arterial, and stop in position near the access or exit until this study; thus more vehicles can change lanes earlier when acceptable gaps occur on both directions on the main road. following their routes, as encountered in the videos. Two thresholds are set for the priority rules to conne fi the crossing of the yielding vehicles, respectively, the minimum 3.5. Measurement of Vehicle Speed Distribution by Feature- headway and the minimum gap time. A yielding vehicle will Based Tracking. Vehicle speed distribution is an important stop before the stop line until both predetermined thresholds input parameter for safety simulation. While potentially more are achieved. The values of the thresholds are determined by accurate, individual vehicle speed on multiple lanes is usually reviewing all the accepted gaps and headways by the crossing dicffi ult to measure on the efi ld simultaneously with radar buses from the video. devices. Therefore, an alternative method was applied in this The conflict areas are automatically generated in VISSIM study to measure the vehicle speed, which is the video-based where links or connectors overlap. In this study, the priority feature tracking. rules at the conflict areas were set to capture the vehicles An open-sourced software project, namely, Traffic Intel- approaching the conflict area from the minor road and ligence, was used to automatically track and measure the yielding those from the main road, as typically observed in speed of thevehiclescaughtbythe videoatthe study 4 Journal of Advanced Transportation Figure 2: Feature tracking process by Tracffi Intelligence . Figure 1: Points selected on the video frame to compute homogra- phy file. site [31]. Traffic Intelligence consists of a set of tools that work cooperatively for tracffi data processing and analysis, including camera image calibration, feature tracking, and trajectory data analysis. 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 The feature-based tracking algorithm utilizes a homogra- Time headway range (s) phy file that projects the camera image space to the real-world Figure 3: Observed vehicle gap distribution. ground plane. The homography file was created by utilizing a video frame and a corresponding aerial photo with known scale (pixels per meter). In this study, an aerial photo of the studysitefromGoogleMapswithknownscaleof0.21pixels model,becausethe vehicletimegapdirectly reflectsthe per meter was adopted. In total ten noncollinear visible points car following behavior. eTh real vehicle gaps were observed on thevideo framewerepositionedonthe aerialphoto;thus, manually from the video using the MPC player that provides the video image was projected to the aerial photo, and the milliseconds accuracy. Because the vehicles travel westbound vehicles tracked in the video were deemed to be tracked in the pass through a signalized intersection before they enter the real-world plane with their speeds. Figure 1 shows the points cameras field of view, to eliminate the impact of the red time projected to the aerial photo from the video frame. at the intersection, the time gaps bigger than 5 seconds were Based on the computed homography file, the feature ignored. The distribution of all the observed gaps that are tracking program can be run. The predetermined number of smallerorequalto 5seconds wasrecordedinahistogram features of each vehicle in the video was detected and tracked with a sample rate of 0.3 seconds. Figure 3 shows the observed frame by frame until the vehicle leaves the video capture vehicle gap distribution. area. In order to suppress the interference of the shadows, a In this paper, the Whale Optimization Algorithm (WOA), mask imagewascreatedand toggledwiththe videoimage; a metaheuristic nature-based algorithm, is applied to cali- therefore only the features within the white range of the brate the model. The deviation of the simulated headway mask image can be detected, and the shadows can be filtered distribution from its’ observed distribution is considered as out. eTh features that move consistently were then grouped the objective function to be minimized during the calibration together to generate the trajectory file of each vehicle, and all process. WOA is inspired by hunting behavior of humpback the trajectories generated from the video were written into a whales. It is defined as “the simulated hunting behavior with database. eTh average speed of each vehicle can be easily read random or the best search agent to chase the prey and the by processing their trajectories. Figure 2 shows the feature use of a spiral to simulate bubble-net attacking mechanism tracking process by Traffic Intelligence . of humpback whales” [32]. eTh hunting behavior of whales is representative of the procedure of this algorithm. 3.6. Model Calibration. In order to determine the optimum The three parameters of the Wiedemann 74 model in values for the calibration parameters, an objective function VISSIM (i.e., average standstill distance (ASSD), additive should be defined based on the error between observed data part of safety distance (APSD), and multiplicative part of andsimulated data.Theobjectivefunction isthedeviation safety distance (MPSD)) which has the highest impact on the of the simulated gap distribution from the real observed modelhavebeenselectedtobecalibrated.UsingMATLAB, gap distribution. In order to test this goodness of tfi (objec- an optimization toolbox connecting to COM-interface of tive), the Chi-square test was employed. In this study, the VISSIM by M-file programming in MATLAB, the calibration westbound vehicle gap distribution on the GP lane near process has been accomplished. Aer ft 190 simulation runs, the bus terminal was taken as the criterion to calibrate the the optimal values of parameters were determined to be as Frequency Journal of Advanced Transportation 5 follows: ASSD = 1.156, APSD = 0.637, and MPSD = 8.079. lane changing, and rear ending, respectively. eTh thresholds of For diverse random seeds, the simulation results showed the conflict angles were adjusted to 2 degree and 45 degree as these optimal parameters lead to statistically matching the suggested by previous studies [8]. Basically, detected conflict observed headway distributions with the simulated ones at which has a conflict angle of 2 degrees or less is defined as 90% confidence level. It is worth mentioning that the simple rear ending conflict; if the conflict angle is between 2 and 45 way to optimize the cost function is exploring the whole degrees, it is detected as lane changing; while if the conflict possible region of the parameters to n fi d the global minimum, angle is bigger than 45 degrees, it is recorded as the crossing which is extremely time-consuming. For this case study, these type. However, due to the peculiarity of geometry of each optimization parameters took values within the following study area, the link information of all the output conflicts, intervals: ASSD between 0 and 2, APSD between 0 and 1, and which was also detected by SSAM, was manually checked to MPSD between 0 and 10. By exploring these intervals, the properly determine their type. The three types of conflicts optimal values were found after nearly 1000 simulation runs. were recorded for subsequent comparative safety analysis. The lateral movement of buses that merge into the main A built-in filter of SSAM can be applied to screen tracffi from HOV lane or travel across the road when an out the conflicts caused by each measured movement by acceptable gap was identified was also calibrated by adjusting reading the corresponding link information. eTh spots where the parameters of the priority rule. eTh minimum gap time conflicts were detected can be plotted automatically on the and distance headway were set to 6 seconds and 20 meters, togglednetwork imagebypositioning theVISSIMnetwork respectively, similar to the values observed in the recorded coordinates. The conflicts of different types can be showed in videos. It is noticeable that a part of the terminal-bound different shapes or colors on the togged map to give a visual buses changed lanes between the reserved HOV lane and estimate of the hotspot areas (i.e., conflicts’ frequency and theadjacentGPlanebeforethe intersection;thisbehavioris density). reflected in the simulation model. 3.9. Summary. The methodology presented in this study 3.7. Simulation Output. VISSIM provides direct output of introduces a simulation-based approach to evaluate road various kinds of simulation results. In this study, the vehicle network safety and efficiency. To apply this methodology, the delay and trajectory were analyzed to evaluate the operational efi ld traffic conditions are collected, and the detailed informa- efficiency and safety of the study area. tion including the efi ld geometry, control strategy, flow, and Vehicledelaydata canbegeneratedby setting vehicle driving behavior is reviewed. Such basic information is then travel time on the defined vehicle routes, which are defined integrated in a VISSIM simulation model. With an important by a Starting Point and an End Point,respectively. Forthe model parameter, the vehicle speed distributions are obtained vehicles that pass through the Starting Point and the End using a feature tracking program, namely, Traffic Intelligence . Point, successively, the travel time delays are automatically The model is properly calibrated until the output vehicle calculated. eTh vehicle delays of the interested vehicle routes time gap distribution compared well with the efi ld observed were then analyzed to evaluate the operational ecffi iency of vehicle gap distribution by applying the Chi-square test. the network. The model output vehicle delays are reviewed for network The trajectories of all the simulated vehicles can be operational efficiency analysis, and the model output vehicle generated by VISSIM, and the recorded trajectory data was trajectory files are analyzed by SSAM to determine the then analyzed using SSAM, to evaluate the vehicle conflicts conflict within the study area thus giving the safety level of within the network. For each simulation run, dieff rent sim- the site. Figure 4 shows the flow chart of the methodology ulation random seeds were applied, and the output results used in this study for traffic safety and operational efficiency were averaged for analysis purposes. This simulation setup evaluation. scenario accounts for the stochastic properties of the simu- lation model, thus reflecting real-world traffic behavior more 4. Case Study realistically. 4.1. Study Area Description. es Th tudyareausedinthis 3.8. Analyzing Vehicle Conicts fl Using SSAM. The vehicle studyisasegmentofRte-116, asuburbanhighwayinLevis, ´ trajectory data collected from VISSIM was used in SSAM Queb ´ ec. Evaluations of traffic safety and operations were to assess the vehicle conflicts detected in the study area. made at a specific location along the four-lane east-west Most studies evaluate tracffi safety through two surrogate arterialsegmentthatincludesoneGPlaneandoneHOVlane, measures, Time to Collision (TTC) and Postencroachment in both directions. eTh eastbound reserved lane allows buses Time (PET). Values below a commonly accepted threshold and passenger cars with three or more passengers, while the of either TTC or PET value indicates a higher probability of westbound direction has a bus-only lane. eTh current design collision. SSAM is able to automatically estimate the TTC and of this facility is such that the westbound buses arriving at or PET values of each vehicle interaction and thus to record all departing from the terminal have to travel across the four- potential conflicts. In this study, the TTC and PET were set to lane undivided road. Figure 5 shows the current paths of the 1.5 seconds and 5 seconds, respectively, the values frequently buses using the terminal. established by previous research studies [20, 33]. The traffic video feeds of vehicles accessing the terminal, The detected conflicts were classified into three types, the commuter parking lot, and traveling along Rte-116 were based on the predetermined conflict angles, namely, crossing, collectedvia GoProHDvideo camerasthatwereinstalled N Bus Bus Bu Bu Bu Bu B B B B B Bu u u u u u u u u us us us us us us us us r re es se erv rv rv rv rv rv rv rv rv rv rv rv rv rv rv v v ve e e e e e e e e e e e e e e e e e e ed l d d d d d d d d d d d d d d d d d d l l l l l l l l l l l l l l l l l l l l l l l l l l l l lan an ane an an an an an an a a a a a a a a an n n n n n n n n n n n n ne e 6 Journal of Advanced Transportation Data collection Basic simulation Speed distribution Observed gap parameters (traffic intelligence) distribution Speed distribution (traffic Intelligence) Model parameter adjusting using optimization method Simulated vehicle gap Pass Chi- No square test Yes Vehicle delay output Vehicle trajectory output Conflict analysis (SSAM) Evaluation results Figure 4: Framework of evaluation procedure. on top of extendable masts along the roadway. Cameras 1 and 2 were both installed at the same location with views opposing each other. The orientations of these two cameras were adjusted to capture east-west traffic that interacts with both access points into and out of the bus terminal. Camera 3 was installed at the proximity of the commuter parking lot entry/exit gate, to capture interactions between main road traffic and vehicles to and from the parking. eTh positions of the cameras are shown in Figure 5. eTh video tracffi data of the PM peak hour (4:30 pm∼5:30 pm) was used in the final analysis of this study. Figure 5: Paths of the terminal-bound buses. A probe vehicle was driven several times along the study segments with an arbitrarily selected constant speed. eTh known speed values were used to calibrate the postprocessing speed detection measuring software, Traffic Intelligence .A fixed 88-second cycle of the tracffi signal along Rte-116 at the distinguished into four types: passenger cars (on the GP adjacent intersection (i.e., 40 seconds, red, 40 seconds, green, lane), buses, trucks, and reserved lane users, respectively. and 4 seconds, yellow) was measured in the eld fi and used in This den fi ition of the tracffi mix was necessary to capture the simulation model of the study area. more reliably the vehicle interactions in the traffic simulation The video files from each camera were processed in 5- model (different vehicle types exhibit different driving behav- minute increments to manually determine the distribution iors in terms of acceleration, minimum headway, etc.). Tables of tracflffi owsduringthe analysis period.Vehicleswere 1 and 2 show a classification of westbound and eastbound Cam 3 Cam 2 Cam 1 Journal of Advanced Transportation 7 Table 1: Observed traffic flow during the peak hour (4:30 pm ∼5:30 pm). Average vehicle flows (vehicles/hour) Time Westbound Eastbound Car Bus Truck HOV Car Bus Truck HOV 4:30 pm∼5:30 pm 663 16 3 7 338 5 4 36 Table 2: Access and egress vehicles during the peak hour (4:30 pm∼5:30 pm). Average vehicle flows (vehicles/hour) Westbound Eastbound Time Bus Parking car Bus Parking car Access Egress Access Egress Access Egress Access Egress 4:30 pm∼5:30 pm 10 14 0 37 7 3 4 18 Table 3: Vehicles characteristics. 2 2 Vehicle type Length (meter) Width (meter) Weight (ton) Maximum acceleration (m/s ) Maximum deceleration (m/s ) Car and HOV 3.75–4.76 1.85–2.07 - 3.5 −7.5 Bus 11.54 3.17 4–12 1.24 −7.5 Truck 13.94 2.63 2.8–40 2.5 −5.5 tracflffi owsalong thehighway,aswellasaccess/egress ofthe buses using the terminal during the aer ft noon peak period. Traffic Intelligence was utilized to measure the vehicle speed. Calibration of the video analysis software was per- formed using various mask pictures to filter the shadows of Bus the moving vehicles until the measured speeds of the probe terminal Parking vehicle were identical to the observed values. eTh vehicle lot speed distributions of both westbound and eastbound vehi- cles were recorded every ve fi minutes and used as simulation input parameters. Figure 6: The status quo network modeled in VISSIM. 4.2. Modeling Existing Configuration and Traffic Conditions (Status Quo). The peak hour tracffi was modeled in VISSIM to evaluate traffic safety and operations of the observed from the current values. Figure 6 represents a snapshot of arterial segment. Vehicle modals used in the simulation are the VISSIM simulation model using the existing geometric selected by VISSIM automatically. The vehicle characteristics alignment and tracffi operations conditions. of thecasestudy areshownin Table3.Toaccountfor the effects of stochastic variation of the model’s parameters, ten different simulations with different random seeds were ran, 4.3. Simulations of Alternative Geometry/Control Designs. andtheaveragevalueswereusedintheanalysis. The main concern related to traffic safety at the investigated The average vehicle delay (excluding signal waiting time study area pertains to the placement of the reserved lanes on at intersection) was measured for three types of movements, the outside lanes. This configuration leads to multiple lanes using the vehicle travel time measurements tool. Movement 1 crossing when left turns are needed and high occurrence of identifies the westbound traffic on the GP lane. Movement 2 is vehicle interactions was observed especially during congested associated with westbound buses entering the terminal (i.e., tracffi conditions. busesmerging from HOVlaneintothe GP laneandthen Two alternative designs have been tested to evaluate crossing the two eastbound lanes). Movement 3 represents their potential to mitigate traffic safety and operations issues. westbound buses leaving the terminal (i.e., buses that cross Figure 7 shows the VISSIM network layout of the first all the four lanes to enter the highway). Vehicle trajectory files alternative design. In this model, westbound buses were were also generated for conflict analysis. prohibited to enter or exit the terminal by crossing the In addition, to evaluate the impact of expected increase highway directly. Instead, an adjacent roadway segment was in tracffi flow on tracffi operations (i.e., average vehicle delay) inserted along the south side of bus terminal, which is directly and safety (i.e., conflicts frequency), the same simulation connected to the minor road. To serve the terminal-bound model was used to evaluate similar scenarios, assuming the buses, ten seconds of left-turning signal phase was provided tracffi volume increases in the future by 10%, 20%, and 30% at the intersection on the main road. Similarly, for each 8 Journal of Advanced Transportation Bus Bus terminal Parking terminal lot Rear end conflict Lane change conflict Crossing conflict Figure 9: Conflicts near bus terminal plots on original network. Figure 7: VISSIM network of alternative road geometry design. Bus terminal Parking lot Movement 1 Movement 2 (buses Movement 3 (buses (westbound roadway into terminal) out of terminal) Figure 8: VISSIM network of alternative control design. traffic) Observed volume Volume increase by 20% Volume increase by 10% Volume increase by 30% traffic demand alternative (i.e., current status, 10%, 20%, and Figure 10: Effects of increasing traffic flow on average delay per 30% increments of vehicular tracffi volume), the collected vehicle. peak hour vehicle flows and speed distributions were used to model the network using ten simulation random seeds. The individual vehicle trajectories and delay measurements of the study area for safety analysis. A built-in filter of SSAM was same movements evaluated for the status quo configuration appliedtoscreenout theconflictscausedbyeachmeasured were collected and used for comparison analysis. Figure 8 movement by reading the corresponding link information. shows the VISSIM network layout of the second alternative design. In this model, a loop detector that controls a signal The spots where conflicts were detected were plotted auto- maticallyonthetogglednetwork imagebyutilizing the set was added to the existing network. This system was used VISSIM network coordinates. eTh conflicts of different types to control the egress of westbound buses as they leave the terminal. eTh add-on signal control model VAP was created were showed in different shapes on the togged map. Figure 9 shows the spatial distribution of conflicts caused by measured to program the signal timing. eTh detector was placed near movements near the bus terminal plotted on the original the exit of the bus terminal. As long as buses are not in the proximity of the sensor, the signal indicates green for network. the main road to allow east-west traffic and red for the bus 4.5. Comparison Analysis of Safety and Operation. Figures 10 exit to prevent the egress buses from traveling across the and 11 represent the impact of different traffic volumes on road directly. When buses are detected at the terminal, exit traffic operations (delay) and safety (conflicts). signal turns green for them and red for traffic on the main road, which allows for protected turns. eTh red signal on the As intuitively expected, more tracffi demand leads to main road lasts for 10 seconds from the last bus detected and increased average delay. It also shows that of the three types then turns back to green until the next detection. eTh same of vehicle interactions analyzed movements labeled 2 and 3 vehicle hourly flows previously processed were used in this (i.e., associated with buses entering and leaving the terminal) simulation scenario, and the same ten different simulation are aeff cted by significantly higher delay than the vehicles random seeds were applied. eTh delay measurements of the moving along the east-west roadway. This is explained by the same types of movements and trajectory data were collected fact that buses have to make left turns from/into the arterial, for comparative analysis. and consequently they do not have the default right of way. In addition, tracffi safety analysis (i.e., evaluation of vehicular 4.4. Surrogate Safety Measures of Vehicle Conicts. fl SSAM interactions through the SSAM tool) shows that, for all levels was applied to assess the vehicle conflicts detected in the of traffic demand, the majority (more than 85%) of vehicular Delay (s/veh) Journal of Advanced Transportation 9 Observed volume Volume increase by 10% Movement 1 (westbound roadway traffic) Movement 1 (westbound roadway traffic) Movement 2 (buses into terminal) Movement 2 (buses into terminal) Movement 3 (buses out of terminal) Movement 3 (buses out of terminal) Volume increase by 20% Volume increase by 30% Movement 1 (westbound roadway traffic) Movement 1 (westbound roadway traffic) Movement 2 (buses into terminal) Movement 2 (buses into terminal) Movement 3 (buses out of terminal) Movement 3 (buses out of terminal) Figure 11: Sensitivity analysis of conflicts distribution (current configuration). conflicts were crossing conflicts associated with the same The sensitivity analysis demonstrates that traffic operations movements of buses that enter or leave the terminal facility. are not impacted by this design. It can be seen that there Moreover, lane-changing conflicts were observed between is a minor positive effect on the average vehicular delay for buses moving from the reserved lane into the GP lane to movement 1 (vehicles traveling westbound on Rte-116), but engage in le-ft turning maneuvers towards the terminal. there is a significant positive effect on the average delay of Figure 12 shows the effects of different traffic volumes on buses accessing the terminal (i.e., a reduction in delay of tracffi operations (magnitude of delay) and safety (frequency about 85%). However, this alternative scenario brings a trade- of conflicts) when the first alternative scenario was used. As off for the movements of buses exiting the terminal that are expected, by including a separation median between the two hindered for most traffic flow levels. The additional delay directions of trac, ffi all vehicular conflicts associated with left- encountered by buses leaving the terminal is due to the fact turn movements into and out of the terminal are eliminated. that, for this design, the westbound egress buses must use 10 Journal of Advanced Transportation −20 −40 −60 −80 −100 Movement 1 Movement 2 Movement 3 (westbound (buses into (buses out of roadway traffic) terminal) terminal) Observed volume −6% −85% 69% Volume increase by 10% −9% −85% 40% Volume increase by 20% −9% −85% 1% Volume increase by 30% −12% −87% −18% Figure 12: Effects of first alternative design on the average delay (separation median). −20 −40 −60 −80 Movement 1 Movement 2 Movement 3 (westbound (buses into (buses out of roadway traffic) terminal) terminal) Observed volume 16% −18% −49% −31% Volume increase by 10% 16% −62% −34% Volume increase by 20% 17% −71% 17% −47% −73% Volume increase by 30% Figure 13: Effects of second alternative design on the average delay (traffic control). the nearby intersection, and the traffic signal timing was different, due to breaking at red light; it is expected that rear not optimized to accommodate left-turning buses from the ending conflicts might be more severe. On the other hand, minor street. rerouting buses through the intersection via the minor street eTh results for the second alternative design (i.e., control- seems to be the best option, because it eliminates completely ling the access/egress of buses for Movements 2 and 3 via all conflicts of le-ft turning vehicles while its impact on a bus-triggered traffic control signal, in order to reduce the traffic operations might not be significant, since it can be vehicle interactions with the buses) are shown in Figure 13. It mitigated with optimizing the traffic signal timing plan at the can be seen that this alternative design reduces considerably intersection. thedelay of busesinand outoftheterminal (Movements2 To conclude, the existing geometric and traffic signal and 3), while it increases by less than 17% the delay of vehicles configurations show that there is a high occurrence of traveling westbound along the arterial (Movement 1). vehicular conflicts for left-turn buses approaching terminal. More importantly, the vehicular conflict analysis of these It canbeseenfromthe resultsabovethat,byusing the results shows the elimination of the crossing conflicts (Move- alternative designs, these types of conflicts are eliminated. ments 2 and 3) related to buses accessing/leaving the terminal In addition, the proposed alterations to existing alignment by turning left across the HOV and GP lanes. In addition, provide benefits for traffic operations because they reduce this design has no impact on the low conflict occurrence of significantly the average vehicular delay. However, when Movement 1 (vehicles moving westbound on the arterial). traffic signals are used to control for protected le-ft turn Several aspects of the proposed alternative designs are buses that are rerouted through the adjacent intersection, discussedattheendofthissection.Thedelay of thetracffi an additional analysis of signal delay and optimization is flow moving westbound on the arterial during the peak necessary. period was compared across all three simulation scenarios Similarly, the analysis of the measured movement 3 (i.e., (i.e., current design, separation barrier, and traffic control westbound buses leaving from terminal) identifies a large alternative). It was found that the tracffi control alternative number of crossing conflicts within the east-west traffic leads to the most negative impact on the vehicular delay. on the main arterial. Elimination of these conflicts can be In addition, conflict occurrence between the current design achieved if this movement is protected either through the and the proposed traffic control design is not signicfi antly tracffi signal sensitive to the buses present at the terminal exit, Relative change of Relative change of Average vehicular average vehicular delay (%) delay (%) Journal of Advanced Transportation 11 or by using the barrier separated geometry that reroutes the Acknowledgments buses via the adjacent intersection. The results indicate that Thisstudy wasfundedbyMinist er ` e des Transports du the network with alternative control design is the best for Que´bec (MTQ) through research Project R706.1. eTh authors departing buses (i.e., the delay is the smallest). wouldliketothank MatinGiahiFoomaniandGiaHungLieu As expected, the sensitivity analysis shows that an from Concordia University for the help in data collection. increased main arterial traffic volume leads to negative effects on the conflict frequency and average vehicular delay, References regardless of the design used, while the alternative designs provide elimination or significant reduction in conflicts. [1] C. Fuhs and J. Obenberger, “Development of high-occupancy vehicle facilities: Review of national trends,” Transportation 5. Concluding Remarks Research Record, no. 1781, pp. 1–9, 2002. [2] M. Menendez and C. F. 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