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|transit signal priority (TSP)] which extends the green phase for an approaching bus or by queue jump treatments 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, trigger 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 ration 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 reduction of green phases for other traffic movements so that overall signal coordination is not affected. Travel time savings can be measured by the number of minute 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. Adriana Rodriguez and Alan R. Danaher, “Operational Comparison of Transit Signal Priority Strategies,” Transportation Research Record No. 2418, 2014, pp. 84-91.

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. One proposal is to combine CTSP with emerging connected vehicle technology (CVT) which allows two way communication 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 TSP 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. 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.

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 resulting in a flex schedule that performed better and faster than other machine learning models and made collecting additional GPS data unnecessary. Tony Hernandez, “Flex Scheduling for Bus Arrival Time Prediction,” Transportation Research Record No. 2418, 2014, pp. 110-115.