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Do Multiple Combinations of Bus Lane Sections Create a Multiplier Effect?: a Micro-simulation Approach
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PAPER NUMBER 15-1661 REVISED SUBMISSION Long Tien Truong* Institute of Transport Studies, Department of Civil Engineering, Building 60, Monash University, Clayton, Victoria 3800, AUSTRALIA Phone: +61 3 9905 1851, Fax: +61 3 9905 4944, Email:
[email protected] Majid Sarvi Institute of Transport Studies, Department of Civil Engineering, Building 60, Monash University, Clayton, Victoria 3800, AUSTRALIA Phone: +61 3 9905 9696, Fax: +61 3 9905 4944, Email:
[email protected] Graham Currie Institute of Transport Studies, Department of Civil Engineering, Building 60, Monash University, Clayton, Victoria 3800, AUSTRALIA Phone: +61 3 9905 5574, Fax: +61 3 9905 4944, Email:
[email protected]
*Corresponding author
Submitted for presentation and publication
Committee: AP050 Bus Transit Systems
Words: 5,139+ ((5 Figures + 4 Tables)*250=2,250) = 7,389 (limit = 7,500)
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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 = 247 words (limit = 250 words)
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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 (1, 2). Numerous studies have focused on the design and evaluation of bus lane priority using empirical, analytical, and simulation approaches (3-5), while a few have attempted to optimize combinations of exclusive transit lanes at the network level (6-8). 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 schemes1. 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 (9). 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 (4). 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 6.8km long in Montreal, Quebec, Canada (10). The results indicated that the reserved bus lanes resulted in savings of 1.3% to 2.2% in total running time and increased the chances of being on time by 65%. 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 (11). 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. (3) 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) (12). 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 (13) 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 (14). Macro-simulation modelling has also been used to examine travel time impacts of reserved bus lanes (15) and set-back bus lanes (5). Considering the wider impact, Currie et al. (16) 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. (17) 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 1
Australian Research Council Industry Linkage Program project LP100100159, ‘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.
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of individual road users and individual vehicle movements on the network (18). 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. (6) 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 (19) and combinatorial optimization of exclusive bus lanes and bus frequencies (20). 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 5.5 kilometers length and five intersections with minor roads (Figure 1). A bus line on the main arterial is eastbound and has 15 bus stops with an average spacing of three stops per kilometer. Table 1 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 (800veh/h/lane in base case) and under-saturated conditions (400 and 600veh/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 67%, in which if random bus dwell time is smaller than 0.1 second, the stop is skipped. This aims to create appropriate variations in bus travel times and bus arrival patterns to traffic signals. (1)
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FIGURE 1 Hypothetical corridor TABLE 1 Simulation Test-bed Characteristics Variable characteristics Design Feature Options Traffic volume on main arterial (V, veh/h) Three levels: V1=1,200, V2=1,800, V3=2,400 Bus headway (F, minute) Two levels: F1=5, F2=10 Fixed characteristics Traffic composition Car = 95%, Heavy goods vehicle (HGV) = 5% Desired speed distributions (km/h) Car (58-68), HGV (54-56) , Bus (54-56) Number of lanes 3-lane each direction (main arterial) 2-lane in crossing direction (minor road) Traffic volume on minor roads = 0.2 Traffic volume on main arterial Turning movements at intersections Main arterial: through (95%), left (3%), and right (2%) Minor roads: through (75%), left (15%), and right (10%) Traffic signal details Fixed-time signals, cycle = 120 seconds. Offset = 0 second Split = 0.7 for main arterial and 0.3 minor for minor roads Bus dwell times Mean=15 seconds, standard deviation=10 seconds. 3
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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 100 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 80 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 2 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 1 to 5. Table 2 suggests that a total of 24 experiments out of 31 possible location combinations (2 5-1) 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 150 micro-simulation scenarios (144 bus lane and 6 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 1% percentage error at 95% confidence level (21) and the number of runs that has already been performed is at least 20. Simulation time is three hours, excluding 30-minute warm-up time. This makes a total of at least 3,000 runs in the experiments. TABLE 2 Bus Lane Combination Experiments Link Number Number of links Designated Locations Number of ( Separate Experiment per Cell) with bus lane experiments 1 1 2 3 4 5 5 2 1&2 1&3 1&4 3&5 4&5 8 2&3 2&4 2&5 3 1&2&3 1&2&4 1&3&5 1&4&5 7 2&3&4 2&3&5 3&4&5 4 1&2&3&4 1&3&4&5 2&3&4&5 3 5 1&2&3&4&5 1 Total 24 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
