Do Multiple Combinations of Bus Lane Sections Create a Multiplier Effect?: a Micro-simulation Approach

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1 Title Page Do Multiple Combinations of Bus Lane Sections Create a Multiplier Effect?: a Micro-simulation Approach PAPER NUMBER - REVISED SUBMISSION Long Tien Truong* Institute of Transport Studies, Department of Civil Engineering, Building 0, Monash University, Clayton, Victoria 00, AUSTRALIA Phone: + 0, Fax: + 0, long.truong@monash.edu Majid Sarvi Institute of Transport Studies, Department of Civil Engineering, Building 0, Monash University, Clayton, Victoria 00, AUSTRALIA Phone: + 0, Fax: + 0, majid.sarvi@monash.edu Graham Currie Institute of Transport Studies, Department of Civil Engineering, Building 0, Monash University, Clayton, Victoria 00, AUSTRALIA Phone: + 0, Fax: + 0, graham.currie@monash.edu *Corresponding author Submitted for presentation and publication Committee: AP00 Bus Transit Systems Words:,+ (( Figures + Tables)*0=,0) =, (limit =,00)

2 0 0 ABSTRACT Numerous studies explore the design and evaluation of bus lane priority using empirical, analytical, and simulation approaches. However, none attempt to understand how different bus lane combinations, such as continuous and discontinuous bus lane sections and a different number of bus lane sections, affect the performance of bus and general traffic. This paper investigates the operational effects of bus lane combinations to establish if multiple bus lane sections create a multiplier effect where a series of continuous bus lane sections create more benefits than several single lane sections. If a multiplier effect exists, it suggests scale economies in wider implementation of bus priority on a network wide scale. Overall, the results confirm there is a multiplier effect i.e. bus travel time benefits and general traffic travel time disbenefits are proportional to the number of links with a bus lane. The effect suggests a constant return to scale on continuous multiple sections. The results also suggest that converting a traffic lane to a bus lane when the upstream traffic volume exceeds the capacity of the remaining traffic lanes causes significant negative impacts for both buses and general traffic. In addition, negative general traffic impacts of continuous bus lane combinations are lower than those for a similar number of discontinuous bus lanes. Interestingly, bus delays at intersections approaching the bus lane tend to be improved when upstream traffic volume does not exceed the capacity of the remaining downstream traffic lanes. Policy implications and areas for future research are suggested. Keywords: Bus lane, Operational performance, Combination effect, Micro-simulation, Transit priority. Abstract = words (limit = 0 words)

3 INTRODUCTION The provision of bus lane priority is justified by not only primary impacts such as improvements of bus travel time and reliability, but also secondary impacts such as mode shift benefits, fleet requirements and operating costs (, ). Numerous studies have focused on the design and evaluation of bus lane priority using empirical, analytical, and simulation approaches (-), while a few have attempted to optimize combinations of exclusive transit lanes at the network level (-). However, simplified traffic flow modelling applied in these optimizing approaches, e.g. static flow-travel time function, is unable to represent dynamic traffic flow impacts of bus lane combinations. No study has attempted to understand the nature of how different bus lane combinations, such as continuous and discontinuous bus lane sections and different number of bus lane sections, affect the performance of bus and general traffic. One interesting question, which this paper aims to explore, is do several combinations of bus lane sections perform better together than they would as a group of individual bus lane sections? This concept might be termed the multiplier effect i.e. is there an effect on bus lane performance which is higher than the sum of individual road segment performance when bus lanes are combined. This paper investigates how combinations of bus lanes act to affect the operational performance of bus and general traffic using a traffic micro-simulation test-bed. It includes a regression analysis of factors influencing bus and traffic performance around combinations of bus lanes to assess if multiplier effects are occurring. The paper is part of a wider research program designed to develop new methodologies to optimise the design and implementation of transit priority schemes. This paper starts with a review of previous empirical, analytical and simulation studies on bus lane priority and its combination. The methodology is then described followed by a review of results and discussion. The paper concludes with a summary of major findings. RESEARCH BACKGROUND The research literature on bus lane priority has primarily concerned with the investigation of travel time savings. An overview of travel time saving evidence can be found in a synthesis report (). Various before-after studies have evaluated the impacts of implemented bus lane priority at a corridor or multiple corridors levels. For example, bus lane impacts, e.g. reductions in running time and standard deviation of running time, were reported in a study in Bangkok, Thailand (). In another study, automatic vehicle location (AVL) and automatic passenger count (APC) data were used to evaluate the impact of reserved bus lane on running times and on-time performance in a corridor of.km long in Montreal, Quebec, Canada (0). The results indicated that the reserved bus lanes resulted in savings of.% to.% in total running time and increased the chances of being on time by %. Whereas these studies focused on changes in total travel time for the whole study corridors, travel times for individual segments, i.e. travel time between two consecutive bus stops, were examined in an empirical study of bus lanes in a major arterial road in Toronto, Canada (). Analysis indicated that travel time benefits are most likely to occur in segments that experienced congestion before the implementation of bus lanes. Black et al. () described an analytical approach for optimal allocation of urban arterial road space, which were based on traffic flow models to calculate user costs for travelling for alternative combinations of mixed traffic lanes, bus lanes and truck lanes. Analysis techniques were also used to study Bus Lanes with Intermittent Priority (BLIP), which is a variant of Intermittent Bus Lanes (IBL) (). In the BLIP, general traffic is forced out of the lane reserved for the bus whereas in the IBL, vehicles already in the bus lane are not required to leave the lane. Using kinematic wave theory, Eichler and Daganzo () studied the feasibility, costs, and benefits of BLIPs. In addition, an extended kinematic wave model with bounded acceleration was developed to focus on impacts of the activation of BLIP strategies on remaining traffic (). Macro-simulation modelling has also been used to examine travel time impacts of reserved bus lanes () and set-back bus lanes (). Considering the wider impact, Currie et al. () proposed an evaluation approach, which employed traffic micro-simulation modelling. The evaluation approach considered a comprehensive list of impacts such as travel time, travel time variability, trip diversion, secondary impact, initial and maintenance costs. Results suggested that viable transit priority schemes require high public transport usage and low levels of traffic usage. In addition, priority designs should avoid situations where turning traffic volumes were significant in the traffic lanes used by transit vehicles. Both macro-simulation and micro-simulation models have been applied in the development of evaluation tools for bus lane priority at a network level. Waterson et al. () described an evaluation approach based on macroscopic traffic models, which considered travel behavior modelling such as rerouting, retiming, modal shift, and trip suppression. Another evaluation approach was proposed using integrated micro-simulation of decisions Australian Research Council Industry Linkage Program project LP0000, Optimising the Design and Implementation of Public Transport Priority Initiatives Institute of Transport Studies, Monash University in association with the Transport Research Group, University of Southampton, UK.

4 0 0 0 of individual road users and individual vehicle movements on the network (). There are two approaches for optimizing combination of bus lanes at the network level. The first approach is to find the optimum combination of exclusive lanes in an existing operational transit network. For example, Mesbah et al. () proposed a bi-level optimization model to search for the optimum combination of priority lanes on the network basis, which considers modal split, traffic and transit assignment. Similar models were proposed to consider redesigning the forth and back routes of each existing bus line () and combinatorial optimization of exclusive bus lanes and bus frequencies (0). The second approach is to consider the optimum combination of exclusive lanes in the context of transit network design problem. However, traffic flow modelling applied in these studies used static functions such as flow-travel time function that does not represent dynamic traffic flow impacts of bus lane combinations. In addition, bus system characteristics such as stops and bus capacity were not modelled in detail. In terms of understanding of the impacts of bus lane combinations, previous studies have several limitations. No study has undertaken a further analysis to understand the nature of how different bus lane combinations, such as continuous and discontinuous bus lanes and different number of bus lane sections, affect the performance of bus and general traffic. METHODOLOGY Modelling test-bed To provide an in-depth understanding of bus lane combinations, a traffic micro-simulation modelling test-bed using VISSIM, based on a hypothetical corridor, is proposed. The setup of the hypothetical corridor is to be generally representative of conditions in suburban Melbourne, Australia where the authors are located. The corridor consists of a main arterial of. kilometers length and five intersections with minor roads (Figure ). A bus line on the main arterial is eastbound and has bus stops with an average spacing of three stops per kilometer. Table provides the required characteristics of the test-bed and identifies variable characteristic tests to be carried out as sensitivity tests on each test-bed experiment. Three levels of traffic volume on the main arterial are examined, including near-saturated (00veh/h/lane in base case) and under-saturated conditions (00 and 00veh/h/lane in base case). Since the main purpose of this study is to understand impacts of bus lane combination on bus and general traffic performance on the main arterial, turning movements from the main arterial are assumed to be small. Moreover, turning movements at intersections are assumed to obtain similar traffic volumes on each sections of the main arterial. Bus dwell times are assumed to be normally distributed with a coefficient of variation of %, in which if random bus dwell time is smaller than 0. second, the stop is skipped. This aims to create appropriate variations in bus travel times and bus arrival patterns to traffic signals. () () () () () 000m 000m 000m 000m 000m 00m FIGURE Hypothetical corridor TABLE Simulation Test-bed Characteristics Variable characteristics Design Feature Options Traffic volume on main arterial (V, veh/h) Three levels: V=,00, V=,00, V=,00 Bus headway (F, minute) Two levels: F=, F=0 Fixed characteristics Traffic composition Car = %, Heavy goods vehicle (HGV) = % Desired speed distributions (km/h) Car (-), HGV (-), Bus (-) Number of lanes -lane each direction (main arterial) -lane in crossing direction (minor road) Traffic volume on minor roads = 0. Traffic volume on main arterial Turning movements at intersections Main arterial: through (%), left (%), and right (%) Minor roads: through (%), left (%), and right (0%) Traffic signal details Fixed-time signals, cycle = 0 seconds. Offset = 0 second Split = 0. for main arterial and 0. minor for minor roads Bus dwell times Mean= seconds, standard deviation=0 seconds. Bus stop

5 Bus lane setup This paper investigates a typical bus lane setup in Melbourne, in which other drivers can drive in a bus lane for up to 00 meters to turn left at intersections (termed a setback bus lane). When the previous link has no bus lane, the bus lane is provided with a departure-side setback of 0 meters to facilitate the transition and merging of general traffic from the three-lane link (no bus lane) to the two-lane link (with a bus lane). Combination scenarios Table identifies the location combination for bus lane experiments. The test-bed has five main arterial links and five intersections each designated by a sequential number of to. Table suggests that a total of experiments out of possible location combinations ( -) can represent continuous and discontinuous bus lane combinations with different numbers of link with a bus lane and different numbers of traffic merging. Together with the base case (no bus lane) and variable characteristics, i.e. three levels of traffic volume and two levels of bus headway, these forms a total of 0 micro-simulation scenarios ( bus lane and base scenarios). To achieve reliable outputs from simulation, a program written in Visual Basic.Net using COM interface of VISSIM runs simulation sequentially until average bus travel time and general traffic travel time are estimated with % percentage error at % confidence level () and the number of runs that has already been performed is at least 0. Simulation time is three hours, excluding 0-minute warm-up time. This makes a total of at least,000 runs in the experiments. TABLE Bus Lane Combination Experiments Link Number Number of links Designated Locations Number of with bus lane ( Separate Experiment per Cell) experiments & & & & & & & & && && && && && && && &&& &&& &&& &&&& Total Analysis approach Effects of bus lane combinations on the performance of bus and general traffic are explored using following key measures of performance. Corridor bus travel time Corridor general traffic travel time, which is computed as the weighted average of car and HGV travel time. Segment bus running time, which is defined as running time between consecutive locations, i.e. stops, start, and end of the main arterial. Coefficient of variation of headway deviations. Since the signal control is fixed time, the impacts of bus lane combinations on the performance of cross street traffic are negligible and therefore not considered in the analysis. T-tests are used to determine whether differences between measures of performances in different scenarios are significant using samples from multiple simulation runs. Furthermore, regression analysis is undertaken to understand effects of bus lane combinations and variable characteristics on bus and general traffic travel time impacts. RESULTS AND DISCUSSIONS Corridor travel times Five-minute bus headway The two-sample t-test is undertaken to compare average corridor bus and general traffic travel times between bus lane scenarios and base scenarios. Results indicate that average bus travel times and general traffic travel times in all bus lane scenarios are significantly different to those in correspondent base scenarios at p<0.00. It is hypothesized that merging three lanes of traffic into two lanes at the start of a road link where a bus lane starts might act to increase traffic delays due to traffic merging. Hence combinations of bus lanes where multiple traffic merges occur might also perform worse for traffic than those with less traffic merges. Hence bus lane combinations with multiple traffic merges, e.g. &, &, &&, might be expected to have worse results than for combinations that have less traffic merges, e.g. &, &&. On inspection of the results, discontinuous bus lane combinations, e.g. &, &, seem to have higher negative impacts for traffic than

6 Bus travel time (s) General traffic travel time (s) Bus travel time (s) General traffic travel time (s) Truong, Sarvi, and Currie continuous bus lane combination, e.g. &, especially when traffic volume is high (Figure ). However, impacts on bus performance are mixed. 0 0 & 0 & 0 & Traffic volume (veh/h) & 0 & 0 & Traffic volume (veh/h) (a) Bus travel time - &, &, & (b) General traffic travel time - &, &, & & & 0 0 & & 0 & 0 & Traffic volume (veh/h) Traffic volume (veh/h) (c) Bus travel time - &, &, & (d) General traffic travel time - &, &, & 0 0 FIGURE Performance of continuous bus lane and discontinuous bus lanes Inspection of the results also suggests different trends between combinations with and without a bus lane on the first link (hereinafter no-bottleneck scenarios and bottleneck scenarios respectively). Average corridor bus and general traffic travel times in no-bottleneck scenarios are presented in Figure a-b, in which zero bus lanes indicate base cases. Changes in bus and general traffic travel times compared to base cases are illustrated in Figure c-d. Bus travel time savings appear to increase in scale by a small amount ( to %) as the number of links with bus lanes increases. Similarly, bus travel time benefits increase with higher traffic volume, indicating that bus travel time benefits are more likely to be obtained with more congested traffic conditions in base case. The results also suggest bus travel time savings with bus lane combinations are of a much smaller scale than the negative travel time impacts on general traffic as illustrated in Figure c-d. General traffic travel time increases with increasing number of links bus lanes and increasing traffic volume. This negative impact is more significant when traffic volume equals,00veh/h, in which traffic squeezed into remaining lanes operating at above,000veh/h/lane. This is consistent with previous research, which suggested that negative impacts on general traffic performance are considerable when traffic volumes per remaining lanes exceed,000veh/h/lane (). Figure e-h indicate different trends in bottleneck scenarios where the combination of high traffic volume (,00veh/h) and bottleneck causes considerable negative impacts for both bus and general traffic. For example, converting a traffic lane to a bus lane and squeezing the traffic volume of,00veh/h into two lanes result in about a 0% increase in bus travel times and a 0% increase in traffic travel times. Figure e and g suggest that providing additional bus lanes can improve bus travel times; however, this improvement is negligible to the negative impacts of congested bottlenecks. Further discussions on this issue are provided in the following sections.

7 (a) Average bus travel time - No bottleneck (b) Average general traffic travel time - No bottleneck (c) Changes in bus travel time - No bottleneck (d) Changes in traffic travel time - No bottleneck (e) Average bus travel time - Bottleneck (f) Average general traffic travel time - Bottleneck (g) Changes in bus travel time - Bottleneck (h) Changes in general traffic travel time- Bottleneck FIGURE Corridor travel times (five-minute headway)

8 minute bus headway Generally the results show similar trends to five-minute headway scenarios. The results of t-tests indicate no significance differences at p<0.0 in bus travel time between five-minute headway scenarios and 0-minute headway scenarios (with the same volume and bus lane combinations). In contrast, t-tests for general traffic travel time tend to be significant at p<0.0 with small number of bus lanes, indicating that 0-minute bus headway scenarios tend to have slightly smaller impacts on general traffic travel time compared to -minute bus headway. The reason may be that smaller bus headways (or more frequent buses) are more likely to create traffic interruptions related to curb-side stops. Segment bus running time An analysis of segment bus running time is undertaken to understand bus running time impacts in different segment conditions such as before, on, and after a bus lane, and segments with and without crossing an intersection. Figure presents changes in segment bus running time between one bus lane scenarios (fiveminute bus headway), e.g. a bus lane on link, respectively (see Figure ), and corresponding base cases. In general, bus running time savings are evident in bus lane segments. The majority of the savings is obtained in the bus lane segment crossing an intersection, e.g. segment 0-00 and segment 0-00 in Figure a and b respectively, while savings in upstream bus lane segments are negligible, indicating that running time saving from bus lanes mainly relates to the improvement in bus delays at intersections. Overall, bus running time savings are greater when traffic volume increases. In segments approaching the bus lane, considerable negative impacts on bus running time are evident with traffic volume of,00veh/h. In this case, providing a bus lane on link or causes a bottleneck and traffic is squeezed into the remaining lanes operating above,000veh/h/lanes. As a result, traffic queues develop further upstream, impeding access of buses to the bus lane. It can be argued that if the length of simulation is long enough, the queues might move back to the start of the main arterial. Interestingly, in the case of lower volume, small bus running time savings are obtained in the intersection segment approaching the bus lane, e.g. segment 0-00 and 0-00 in Figure a and b respectively. It can be argued that with lower traffic volume, traffic merging occurs further upstream of the bottleneck and hence less traffic will use the curb-side lane, which facilitates bus movements to the bus lane downstream. This is supported by empirical studies, which indicates that when the traffic volume is low, the likelihood for drivers to use the transition section after the lane drop intersection for merging decreases (). Furthermore, changes in bus running time are evident with both benefits and disbenefits in the following intersection segments where there is no bus lane, e.g. segments 0-00 and 0-00 in Figure a and segment 0-00 in Figure b. These mixed impacts are suggestive of the issue of bus sequencing through signalized arterials. Changes in bus travel time in upstream links arguably might affect bus arrival time distributions in downstream intersections, leading to changes in bus delays at downstream intersections. The queue spillback phenomenon of converting a traffic lane to a bus lane is further described using the cumulative curves in Figure. N(x,t) is defined as the cumulative number of vehicles passing detector x by time t. It is worth noting that cumulative curves in Figure only consider through traffic for the whole arterial. Detector is located at the beginning of the main arterial while detectors - are located at stop lines of the following intersections - respectively. Each curve for detectors - is translated by the average free flow travel time from its respective location to detector, the most downstream detector. Vertical differences between curves are the excess vehicle accumulation because of traffic delays (). Figure a indicates that when traffic volume is,00veh/h, traffic queue forms on link (with a bus lane) as excess vehicle accumulations occur between detectors and. Traffic queue then moves backward to link indicated by excess vehicle accumulations between detectors and. It is evident that traffic queue is more severe on link where traffic merges from three lanes to two lanes. In addition, the divergence of the curve at detector from the one at detector reveals that queue is growing on link. When traffic volume is,00veh/h, there is no backward moving queue as queues resulting from traffic signals appear to be cleared in the following cycles (Figure b). Headway adherence Individual bus trajectories obtained from multiple simulation runs are used to calculate coefficient of variation of headway deviations (CV h ) for each stop and for the bus line. The results indicate no significant differences in CV h amongst scenarios and generally CV h is less than 0., indicating the level of service A ().

9 (a) A bus lane on link (b) A bus lane on link FIGURE Changes in segment bus running times

10 a) A bus lane on link and traffic volume=,00veh/h b) A bus lane on link and traffic volume=,00veh/h FIGURE The cumulative number of vehicles

11 0 0 0 Regression models To provide a better understanding of impacts of bus lane combinations on the performance of bus and general traffic under variable characteristics, regression analyses are undertaken. Descriptions of variables used the regression models are presented in Table. The selection of variables is informed by observation and discussion in previous sections. It is worth noting that volume-delay functions in literature suggest a non-linear relationship between general traffic disbenefits and traffic volume. For instance, the flow-travel time function introduced by the Bureau of Public Roads indicates that general traffic disbenefits would be proportional to volume β with β= (). Therefore, the non-linear form volume is also considered in regression models. TABLE Description of Variables Used in the Regression Models Variables Description Dependent variables Bus travel time change Change in corridor bus travel time compared to base case (%) General traffic travel time change Change in corridor general traffic travel time compared to base case (%) Independent variables Bus lane Number of links with a bus lane Volume Traffic volume on the main arterial (,000veh/h) Congested bottleneck Dummy, if traffic volume=,00veh/h and no bus lane on the first link Congested merging Equals the number of traffic merges if traffic volume=,00veh/h, otherwise 0 Results of regression models are presented in Table. The best-fit model for changes in bus travel time is the model significant with adjusted R of 0.. The best-fit model for changes in general traffic travel time is the model significant with adjusted R of 0.. Collinearity tests suggest no evidence of collinearity. TABLE Results of Linear Regression Models Variables Bus travel time change General traffic travel time change Model Model Model Model Model Model B B B B B B Constant Congested bottleneck Congested merging Bus lane Volume Bus lane*volume Bus lane*volume Adjusted R RMSE Note: B = coefficient, RMSE = root mean square error, N=, all variables significant at p<0.0 In the best-fit model for bus travel time change (model ), bus lane*volume is significant with a coefficient of -0.00, implying a multiplier effect of bus lane combinations is occurring. The form of this link is generally a constant or linear return to scale. The relationship between bus benefits and traffic volume is approximately linear. For example, when traffic volume is,00veh/h and,00veh/h, an additional bus lane tends to improve bus travel times by 0.% and % respectively. In the best-fit model for general traffic travel time change (model ), bus lane*volume is significant with a coefficient of This indicates a non-linear relationship between general traffic disbenefits and traffic volume, which is consistent with previous research (). Providing an additional bus lane tends to increase general traffic travel times by 0.% and.% with traffic volume of,00veh/h and,00veh/h respectively. It is evident that bus travel time benefits are of a much smaller scale than general traffic disbenefits when traffic volume is high. Congested bottleneck has significant influences on bus and general traffic travel times with coefficients of 0. and 0. for the model and respectively, which suggests a larger impact on general traffic. Converting a traffic lane to a bus lane when inflow traffic volume exceeds capacity of the remaining traffic lanes has significant negative impacts on both bus and traffic performance. Furthermore, providing additional bus lanes is unlikely to reverse bus travel time disbenefits resulting from congested bottleneck (about %). Congested merging is significant in the model, indicating that the number of traffic merges tends to increase general traffic travel time with high traffic volume. This suggests that with high traffic volume, discontinuous bus lane combinations tend to have larger negative impacts on traffic when compared to continuous bus lane combinations (with the same number of bus lane sections). 0

12 CONCLUSION This paper investigates trends in the operational effects of bus lane combinations on bus and general traffic using a traffic micro-simulation test-bed. The test-bed is based on a hypothetical corridor with typical setups of Melbourne suburban conditions. Overall, the results indicate a multiplier effect of bus lane combinations, i.e. bus travel time benefits and general traffic travel time disbenefits are proportional to the number of links with a bus lane. Results suggest that converting a traffic lane to a bus lane when the upstream traffic volume exceeds the capacity of the remaining traffic lanes causes significant negative impacts for both buses and general traffic. Bus travel time benefits and general traffic travel time disbenefits are greater with higher traffic volume. While the relationship between general traffic disbenefits and traffic volume is non-linear, the relationship between bus benefits and traffic volume is approximately linear. Furthermore, negative general traffic impacts of continuous bus lane combinations are lower than those for a similar number of discontinuous bus lanes. Interestingly, bus travel time impacts of continuous and discontinuous bus lane combinations are mixed. The results also reveal different patterns in bus running time savings in segments before, after, and on a bus lane. For bus lane segments, bus travel time savings mainly relates to improvements in bus delays at intersection. For non-priority segments approaching the bus lane, bus travel time impacts are negative when inflow traffic volume to bus lane segments exceeds the capacity of the remaining traffic lanes whereas with lower traffic volume, these impacts tend to be positive in the intersection segment approaching the bus lane. These positive impacts are attributable to the phenomenon that with lower traffic volume drivers tend to merge further upstream of the bottleneck, leading to less traffic on the curb-side lane used by bus to travel to the downstream bus lane. The results also suggest impacts of signal coordination on the performance of bus lane combinations. Approaches to optimize signal offsets, e.g. (), can be used to maximize benefits of bus lane combinations. The paper confirms that a multiplier effect affects bus performance when combinations of bus lane sections are added together with a linear return to scale. This implies the grouping of bus lanes in a continuous series has higher benefits than a series of individual unconnected lanes. From a policy perspective it suggests a network wide scale rather than a local scale for bus lane priority provision has considerable merits. Limitations of the paper relate to the relatively narrow range of variable characteristics of the test-bed that is based on a hypothetical corridor. Future works will explore these trends in the operational performance of bus lane combinations using empirical approaches. ACKNOWLEDGEMENTS The authors would like to thank the Australian Research Council, VicRoads, and Public Transport Victoria for sponsoring the research. The first author is also supported by the Prime Minister s Australia Asia Award and the Directorate for Roads of Vietnam. Any omissions or errors in the paper are the responsibilities of the authors. REFERENCES. TCRP. Bus Rapid Transit, Vol. : Implementation Guidelines. TCRP Report 0. Transportation Research Board, Transit Cooperative Research Program, Washington, D.C., 00.. G. Currie, and M. Sarvi. New Model for Secondary Benefits of Transit Priority. In Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academics, Washington, D.C., 0, pp. -.. J. A. Black, P. N. Lim, and G. H. Kim. A traffic model for the optimal allocation of arterial road space: a case study of Seoul's first experimental bus lane. Transportation Planning and Technology, Vol., No.,, pp Y. Tanaboriboon, and S. Toonim. Impact Study of Bus Lanes in Bangkok. Journal of Transportation Engineering, Vol. 0,, pp. -.. D. Jepson, and L. Ferreira. Assessing travel time impacts of measures to enhance bus operations. Part II: Assessment criteria and main findings. Road and Transport Research, Vol., No., 000, pp. -.. M. Mesbah, M. Sarvi, and G. Currie. New Methodology for Optimizing Transit Priority at the Network Level. In Transportation Research Record: Journal of the Transportation Research Board, No. 0, Transportation Research Board of the National Academics, Washington, D.C., 00, pp M. Mesbah, M. Sarvi, and G. Currie. Optimization of Transit Priority in the Transportation Network Using a Genetic Algorithm. Intelligent Transportation Systems, IEEE Transactions on, Vol., No., 0, pp M. Mesbah, M. Sarvi, I. Ouveysi, and G. Currie. Optimization of transit priority in the transportation network using a decomposition methodology. Transportation Research Part C: Emerging Technologies, Vol., No., 0, pp. -.

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