Bus prioritization

Introduction

One strategy to reduce travel times and provide more reliable transit service is through bus prioritization strategies. These can take a number of forms including dedicated bus-only lanes, bus rapid transit, bus bypass shoulders, 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.

Transit Signal Priority

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 on other traffic.

Exclusive and Intermittent Bus Lanes

Another alternative to improve bus speed and reliability is to allow buses either exclusive or preferential use of a travel lane. This allows the bus to skip the car queues and minimizes the bus delay experienced at the signal. However, especially in areas where bus traffic is light, this approach can result in under use of valuable roadway space and lead to longer car queues at signalized intersections. As a result, even though bus passengers may enjoy reduced travel times, the total amount of delay in the system can increase as fewer cars are able to cross the intersection on the green light. Bus lanes may also interfere with cars executing right turns at the intersection.

Use of Presignals

One way that has been proposed that gives buses priority but still utilizes the full capacity of green signals is the use of a presignal place upstream of the main signal.

Optimizing Priority Lanes

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.

This study proposes a new logic to overcome adverse effects of TSP using connected vehicle technology, including two-way communications between buses and the traffic signal controller, to generate accurate bus location information and data on number of passengers. The key feature is green time reallocation, which moves green time instead of adding extra green time, in response to overall person delay on the system. The proposal is then evaluated using both analytical and microscopic simulation approaches. 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, with no significant negative effects.

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

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 for predicting bus arrival times, concluding that the use of computationally intensive machine learning algorithms, such as support vector regression, k nearest neighbor regression, and neural networks, is unnecessary. Simpler linear models combined with the real-time GPS bus location information could be used to determine explicitly the approximate historical interstop travel times for any time of the day and any day of the week, resulting in a flex schedule that was independent of scheduled departure or arrival times, and obviating the need for additional data collection.

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

General traffic can interfere with buses operating in mixed traffic and cause reductions in travel speed and system capacity. This paper presents a methodology for evaluating the impacts of TSP treatments on transit operations at a specific intersection by comparing various TSP options to determine which 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.

This paper quantifies the benefits on traffic flow of using presignals in terms of reducing systemwide total person hours of delay, specifically for oversaturated intersections. Results showed that presignals provided the lowest delay compared with a dedicated lane or mixed lane strategy, and 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.

Yuval Hadas and Avishai (Avi) Ceder, "Optimal Connected Urban Bus Network of Priority Lanes," Transportation Research Record No. 2418, 2014, pp. 49-57.

This paper presents a new model for selecting an optimal network of public transport (PT) priority lanes that would increase the reliability of transfers and provide better adherence to schedule performance. The study model was designed to maximize total travel time savings and, at the same time, maintain balanced origin and destination terminals, given budget constraints. It was used successfully in a case study of Petah Tikva, a midsize city in Israel, to produce an optimal network of priority lanes.