Bus prioritization

Introduction

One strategy to reduce travel times and provide more reliable transit service is through bus prioritization strategies. These can take the form of transit signal priority (TSP) which extends the green phase for an approaching bus or by queue jump treatments that permit buses and other vehicles in the far right turn or bus-only lane to proceed ahead of traffic in adjacent lanes. TSP can either be pretimed, triggered by an approaching bus, or the signal can be adjusted based on real time monitoring of traffic patterns. This strategy is appropriate for intersections operating under LOS C or D and with a volume/capacity ratio less than 1.0, otherwise the longer queues will prevent bus from clearing the intersection. The additional time allotted for buses is achieved through slight reductions in green phases for other traffic movements so that overall signal coordination is not affected. One drawback of conventional TSP (CTSP) strategies is that it is based on sensors that may not provide accurate bus arrival time information to decide whether to shorten the red phase or extend the green. As a result, there could be a waste of extra green time and unnecessary delays affecting side streets.

Measuring Benefits

Travel time savings can be measured by the number of minutes of reduced delay per mile of operation or per person. Even if detailed simulation modeling is not practical, simple sketch planning tools can be used to evaluate the optimal strategy for specific corridors. Cost benefit analysis can then be conducted to determine it the necessary capital improvements, such as lengthening auxiliary lanes to reduce queuing, are warranted.

Intelligent TSP

One proposal to improve bus operations is to combine CTSP with emerging connected vehicle technology (CVT) which allows two way communications between buses and traffic signal facilities and can collect more accurate information based on automatic vehicle location (AVL) systems. This TSP with CV (TSPCV) environment can supplement existing data with measurements of vehicle speed, position, acceleration and deceleration, queue lengths, arrival time, dwell time and number of passengers. In addition to simple red signal truncation and green light extension, intelligent TSPCV can reallocate green time to when it will most benefit bus movement rather than just adding time, and thus minimize adverse impacts on non-transit vehicle travel especially on intersecting side streets. Selective priority can be granted or withheld depending on factors such as whether buses are running on time or delayed, and the number of onboard passengers, in order to minimize total person delay across all modes. Bus speeds can also be adjusted to take better advantage of TSP. A recent study by the University of Virginia simulating traffic at a selected intersection found that TSPCV improves the reliability of bus service and could reduce bus delay by nearly 90% compared to less than 13% for CTSP. Benefits decline as traffic volume approaches capacity since the proposed algorithm is designed to reduce the amount of green time granted to buses to prevent extra delay to other travel, but this minimizes overall person delay. The authors conclude that this next-generation TSP could greatly reduce bus delay at signalized intersections without causing negative effects to other traffic.

Predicting bus arrival times can be done using a variety of techniques including linear models, neural networks, vector regression, and k nearest neighbors regression. A recent study concluded that using linear models to estimate interstop travel times combined with real-time GPS information on current vehicle location resulted in a flex schedule that performed better and faster than other machine learning models and made collecting additional GPS data unnecessary.

Further Reading

Jai Hu, Byungkyu (Brian) Park, and A. Emily Parkany, “Transit Signal Priority with Connected Vehicle Technology,” Transportation Research Record No. 2418, 2014, pp. 20-29.

Transit signal priority (TSP) has been studied as a control strategy that offers preference to transit vehicles at signalized intersections. Although TSP has been deployed in many places, several shortcomings, such as adverse effect on side streets and uncertainty about the benefit, have been identified. Therefore, a new TSP logic proposed to overcome these shortcomings takes advantage of the resources provided by connected vehicle technology, including two-way communications between buses and the traffic signal controller, accurate bus location detection and prediction, and number of passengers. The key feature of the proposed TSP logic is green time reallocation, which moves green time instead of adding extra green time. TSP is also designed to be conditional. That is, delay per person is used as the most important criterion in deciding whether TSP is to be granted. The logic developed in this research was evaluated in two ways: with analytical and microscopic simulation approaches. In each evaluation, the proposed TSP was compared with two scenarios: no TSP and conventional TSP. The analysis used bus delay and per person delay of all travelers as measures of effectiveness. The simulation-based evaluation results showed that the proposed TSP logic reduced bus delay between 9% and 84% compared with conventional TSP and between 36% and 88% compared with the no-TSP condition. The range of improvement corresponding to four volume-to-capacity ratios was tested. No significant negative effects were caused by the proposed TSP logic.

Tony Hernandez, “Flex Scheduling for Bus Arrival Time Prediction,” Transportation Research Record No. 2418, 2014, pp. 110-115.

The prediction of bus arrival times is an important element for travel planning. This study used three weeks of Chicago, Illinois, Transit Authority bus route GPS data to compare the performance of several commonly used methods and algorithms. The use of implicit schedules in previous papers was inadequate. The use of additional information, such as recent travel times along the route, is unnecessary. In addition, the use of computationally intensive machine learning algorithms, such as support vector regression, k nearest neighbor regression, and neural networks, is unnecessary. The study used basis expansion functions at various resolutions with linear models and cross-validated the models to determine explicitly the approximate historical interstop travel times for any time of the day and any day of the week. Combining the estimated interstop travel times with the real-time GPS location of a bus resulted in a flex schedule that was independent of scheduled departure or arrival times. Using a flex schedule makes the use of additional GPS information or the use of the machine learning algorithms unnecessary.

Adriana Rodriguez and Alan R. Danaher, “Operational Comparison of Transit Signal Priority Strategies,” Transportation Research Record No. 2418, 2014, pp. 84-91.

Priority strategies for bus transit are implemented with the purpose of expediting transit flow by reducing travel times and delay while providing a more reliable system that may attract new riders and increase transit capacity. Such strategies are a critical component of bus rapid transit, although the strategies can also be applied to regular local bus operations. The interference of general traffic when buses are operating in mixed traffic results in a reduction in speed and capacity. Intersection priority treatments reduce signal delay and result in travel time savings to transit. Transit signal priority (TSP) strategies may be applied across several intersections, depending on the level of service and lane configuration characteristics of the signalized intersections along a corridor, and can be combined in the same signal operation for each approach serving transit. This paper presents a methodology for evaluating the impacts of TSP treatments on transit operations at a specific intersection by comparing various treatment options. The effectiveness of the transit priority treatment was measured by the travel time savings for bus operations given in minutes of reduced delay. The applicability of this methodology in the planning of TSP strategies for transit service is explained in two sample calculations, and the results are compared to determine the feasibility of the TSP treatment that would give the highest travel time savings for signalized intersections along the study corridor.

S. Ilgin Guler and Monica Menendez"Evaluation of Presignals at Oversaturated Signalized Intersections," Transportation Research Record No. 2418, 2014, pp. 11-19.

One of the major causes of bus delays in urban environments is signalized intersections. A commonly used solution to give priority to buses at signalized intersections is to dedicate a lane for bus use only. This strategy allows the bus to skip the car queues and minimizes the bus delay experienced at the signal. However, especially for low bus flows, the strategy can waste valuable green time at signals and impose additional car delays. Overall, even when bus passengers enjoy reduced travel times, the total person hours of delay in the system can increase. To avoid this problem and utilize the full capacity of the main signal while still providing bus priority, the use of a presignal has been proposed. The goal of this research was to quantify the benefits of the use of presignals in systemwide total person hours of delay, specifically for oversaturated intersections. Theoretical formulas were developed to quantify the effects of a presignal on traffic flow, and the formulas were empirically verified. The theoretical model was used to compare the total delay with a presignal strategy with the total delay with a dedicated bus lane or fully mixed lanes. Bounds on bus-to-car occupancy ratios were quantified for which presignals provided the lowest delay compared with a dedicated lane or mixed lane strategy. The results showed that for oversaturated intersections, presignals were better for the system than dedicated bus lanes. Moreover, presignals could decrease the total person hours of delay compared with mixed lanes for large car demands.