Joint optimization of tram timetables and signal timing adjustments at intersections

The continuous growth of automobile traffic on urban and suburban arterials in recent years has created a substantial problem for transit, especially when it operates in mixed traffic conditions. As a result, there has been a growing interest in deploying Transit Signal Priority (TSP) to improve the operational performance of arterial corridors. TSP is an operational strategy that facilitates the movement of transit vehicles (e.g., buses) through signalized intersections that helps transit service be more reliable, faster, and more cost-effective. The goal of this research was to quantify the mobility and safety benefits of TSP. A microscopic simulation approach was used to estimate the mobility benefits of TSP. Microscopic simulation models were developed in VISSIM and calibrated to represent field conditions. Implementing TSP provided significant savings in travel time and average vehicle delay. Under the TSP scenario, the study corridor also experienced significant reduction in travel time and average vehicle delay for buses and all other vehicles. The importance and benefits of calibration of VISSIM model with TSP integration were also studied as a part of the mobility benefits. Besides quantifying the mobility benefits, the potential safety benefits of the TSP strategy were also quantified. An observational before-after full Bayes (FB) approach with a comparison-group was adopted to estimate the crash modification factors (CMFs) for total crashes, fatal/injury (FI) crashes, property damage only (PDO) crashes, rear-end crashes, sideswipe crashes, and angle crashes. The analysis was based on 12 corridors equipped with the TSP system and their corresponding 29 comparison corridors without the TSP system. Overall, the results indicated that the deployment of TSP improved safety. Specifically, TSP was found to reduce total crashes by 7.2% (CMF = 0.928), FI crashes by 14% (CMF = 0.860), PDO crashes by 8% (CMF = 0.920), rear-end crashes by 5.2% (CMF = 0.948), and angle crashes by 21.9% (CMF = 0.781). Alternatively, sideswipe crashes increased by 6% (CMF = 1.060), although the increase was not significant at a 95% Bayesian credible interval (BCI). These results may present key considerations for transportation agencies and practitioners when planning future TSP deployments.

Transit signal priority (TSP) is a signal timing strategy to give priority to transit by adjusting the signal operation with the goal of reducing transit delay and improving reliability. While TSP can be a powerful tool, TSP deployments in the U.S. have often resulted in marginal improvements. The primary reasons for limited TSP effectiveness are short detection horizons for TSP requests (e.g., 10 s), near-side bus stops (i.e., located before crossing an intersection) that influence arrival times at the downstream traffic signal, and restrictive signal timing strategies (e.g., lock-out policies that inhibit TSP for a specified amount of time, coordinated control that offers little flexibility for TSP). This paper documents the impacts of a “next-generation” TSP system that couples with custom signal control logic for TSP through a field deployment in Portland, Oregon, U.S., using emerging data sources. The system uses cloud-based, predictive logic for estimating time of arrival, with predictions of bus arrivals available up to 2 min ahead of each intersection and updated continuously every 1 s. The custom signal control logic includes advanced TSP strategies that can take advantage of early prediction. Using data from high-resolution automatic vehicle location, analysis results show the custom signal controller logic with advanced prediction resulted in an average bus delay reduction of 29 s per intersection at major intersections (a reduction of 69% compared with baseline). Analyses using automated traffic signal performance measures and vehicle probe data showed these bus delay improvements were achieved with marginal impacts on motorists and without additional delay to pedestrians and bicycles.

Transit Signal Priority (TSP) makes Public Transport (PT) more competitive to the private car by facilitating the movement of PT vehicles across signalised intersections. However, the acceptance of TSP is limited, as its application can result in delays to the general traffic. In the future traffic signals will broadcast Signal Phase and Timing (SPaT) status information to nearby vehicles. Thanks to this, TSP could be complemented with in-vehicle systems such as Green Light Optimal Speed Advisory (GLOSA) and Green Light Optimal Dwell Time Advisory (GLODTA). They allow a bus to pass through signalised intersections during a green phase, without modifying signal timings. Therefore they do not create delays to the general traffic. The objective of this paper is to analyse how the two systems can complement TSP and to which extend they can be used instead of TSP. Using computational experiments the authors demonstrate that although signal priority provides greater travel time improvement, the two advisory systems offer similar advantages in terms of reducing the number of stops and delay time at traffic signals.

Transit signal priority (TSP) has become popular among cities in the United States as a transit preferential treatment that improves transit operational performance through adjusting signal timing when transit vehicles are approaching signalized intersections. The majority of attention has been given to the operational effects of TSP. A small number of prior studies have discussed such a system’s effects on traffic safety, with few studies investigating North American implementations. Prior assessment results were mixed, leaving the understanding of TSP’s safety effects unclear. The purpose of this study was to add to the existing research on this topic, and to get a better understanding of TSP’s safety effects. An empirical Bayes (EB) before–after analysis was carried out using TSP implementation data from King County, Washington. Eleven transit corridors having had TSP activated in 2010–2014 were assessed, and 75 street sections without TSP treatment were selected to form a reference group. The EB analysis results showed a 13% reduction in total crashes with the implementation of TSP, a 16% reduction in property-damage-only crashes, and a 5% reduction in fatal and injury crashes. These results are consistent with most existing TSP safety studies. The study implies that TSP brings road safety benefits to transit corridors in a U.S. metropolitan setting, and suggests that transportation and transit agencies add safety assessment into future TSP practices.

Global positioning system (GPS)-based transit signal priority (TSP) has advantages in providing flexible and conditional signal priority to transit vehicles. This study compared the effectiveness of different combinations of operational patterns and TSP strategies and assessed their impacts on side-street traffic operations. Micro-simulation analytical tools were applied to two testbeds of Utah Transit Authority’s Bus Route 33 and bus rapid transit (BRT) Line 35 MAX. Eight scenarios were created for Route 33 that included the existing conditions and combinations of regular/BRT operational patterns with traditional TSP, GPS-based TSP, conditional TSP implementations. Three scenarios were created for 35 MAX, also considering the existing traditional TSP condition, GPS-based TSP, and conditional TSP implementations. Results confirmed similar operational effectiveness of GPS-based TSP when compared to traditional TSP. Conditional TSP had smaller impacts on side-street traffic while providing transit vehicles considerable amounts of delay reduction and travel time saving.

Driven by the growing demand for transportation, research on how to alleviate urban traffic congestion has received increasing attention, while public transportation is an effective way to solve the problem of urban congestion. As an important means to improve the speed and reliability of the public transportation system, the transit signal priority (TSP) control system has been widely deployed for the last few decades. With the emergence and development of V2X technology, it provides a wider range and more accurate data for the real-time TSP strategy, which has made the TSP control strategy have a new development. In this paper, a V2X (Vehicle to Everything) based TSP control strategy was proposed for a single intersection. Enabled by V2X communication, bus can send a priority request before entering the intersection, activating the real-time signal priority control of the front intersection before the bus reaches the intersection. In this TSP strategy, the travel time was estimated based on the shock wave theory. Finally, the field test was designed and implemented to test the performance of V2X communication and analyze and verify the proposed TSP strategy.

Bus rapid transit (BRT) as a valid means of public transportation for overpopulated country of China is an especial mode of travel to relieve traffic congestion. Despite utilizing exclusive bus line, BRT constantly experiences delay at intersections when encountering a red signal. Transit signal priority (TSP) considered as a promising control strategy to improve the operation efficiency of BRT has been widely used at isolated intersections or arterials to reduce travel delay. However, as a transport with schedule, the unexpected arrival time of transit vehicle at stop lines of consecutive intersections inevitably affects the operation reliability of the BRT system. To solve the problem, this study develops an optimization model of conditional TSP for bus rapid transit with the objective of improving the on-time performance of the BRT system while mitigating adverse impacts on private vehicles. A stop-to-stop segment is established with both road sections and intersections for the consideration of the transit operation reliability. The constraints on the degree of saturation and queue overflow were considered for mitigating adverse impacts of TSP. A mixed-integer non-linear programming procedure is adopted to formulate the optimization model. The mathematical model is linearized and solved by the branch-and-bound method. Extensive numerical analyses are conducted, and the proposed conditional TSP strategy is compared with unconditional TSP and no-TSP control to evaluate its performance under various traffic and signal conditions. A case study of a BRT line in Shanghai, China, was selected to illustrate the effectiveness of the proposed model. For the tested scenarios, the transit on-time performance was improved by 21%, with an incurred cost of 3.4% increase in the delay of private vehicles. This indicates that the proposed model performs well in maintaining the reliability of the transit system with the least impact on general traffic.

The design of transit signal priority (TSP) systems requires knowledge of dwell-time distributions at bus stops within the block. Dwell-time trends are not well established in the literature despite the ubiquity of large sets of automated passenger count (APC) data. Additionally, the impact of dwell-time variability on TSP performance is not well studied, particularly with field-collected dwell-time data. This study first analyzes trends and distributions inherent in dwell-time data deduced from APC data. Dwell times vary from stop to stop and for each stop by time of day (TOD). Stops with the highest proportions of non-zero dwell time also had the highest dwell-time magnitudes and variability. For most stops, dwell-time data was closely fitted by inverse Gaussian, log-normal, power log-normal, Fisk (log-logistic), and Johnson’s SU distributions. The second part of the study used a simulation environment to evaluate the impact of dwell-time magnitude and variability on TSP performance at both far-side and near-side bus stops. For far-side bus stops, dwell time significantly impacted the bus arrival profile at the check-in detector and thus the selected TSP strategy. Higher dwell-time magnitudes and variability led to a higher share of an Early Green Phase (EG) which is not as effective as a Green Phase Extension (GE). At near-side bus stops, dwell-time variability induced more uncertainty in an estimated time of arrival (ETA) and significantly reduced TSP effectiveness especially for GE. TSP performance in terms of GE success, bus travel time, and side-street traffic delay was significantly better at far-side stops compared to near-side stops.

본 연구에서는 충격파 모형을 이용하여 능동식 우선신호의 최적 신호시간을 산정하기 위한 모형을 제시하였다. 본 신호 최적화 모형을 이용하여 능동형 우선신호 기법 중 Early green 및 Green extension이 적용되는 조건에서 충격파 면적을 산정할 수 있다. 본 연구에서는 평균통행시간 및 교차로 진출시각을 이용해 충격파의 발생 속도를 산정하기 위한 방법을 제시하였으며, 이를 이용해 우선신호로 인한 현시 변화량에 따라 충격파 면적 변화량을 산정할 수 있다. 또한 교차로 전체의 충격파 면적이 최소화되는 신호시간을 산정하여 우선신호로 인해 증가하는 일반차량의 지체를 최소화할 수 있도록 하였다. 우선신호 신호시간 산정 모형의 효과평가를 위해 VISSIM과 ComInterface를 이용한 미시적 시뮬레이션 분석을 시행하였으며, 이동류의 포화상태를 고려하여 지체 최소화를 위한 신호시간이 산정됨을 확인하였다. 독립교차로를 대상으로 하는 사례분석에서 우선신호를 위해 비우선현시를 균일하게 단축하는 전략 대비 본 모형에서 일반차량 지체가 10% 이상 개선됨을 확인하였다. 본 연구는 트램, BRT, 중앙버스 전용차로 등 대중교통 우선시설이 확산되고 있는 최근 국내 상황에서 신호교차로의 운영효율을 높이기 위한 새로운 우선신호 제어 방법을 제시하였다는데 의의가 있겠다. This research suggested the traffic signal calculation model of active transit signal priority using a shockwave model. Using this signal priority timing optimization model, the shockwave area is computed under the condition of Early Green and Green Extension among active transit signal priority techniques. This study suggested the speed estimation method of backward shockwave using average travel time and intersection passing time. A shockwave area change is calculated according to signal timing change of transit signal priority. Moreover, this signal timing calculation model could determine the optimal signal priority timings to minimize intersection delay of general vehicles. A micro simulation analysis using VISSIM and its user application model ComInterface was applied. This study checked that this model could calculate the signal timings to minimize intersection delay considering saturation condition of traffic flow. In case studies using an isolated intersection, this study checked that this model could improve general vehicle delay of more over ten percentage as compared with equality reduction strategy of non-priority phases. Recently, transit priority facilities are spreading such as tram, BRT and median bus lane in Korea. This research has an important significance in that the proposed priority model is a new methodology that improve operation efficiency of signal intersection.

Transit signal priority (TSP) is the most effective method to improve the operational efficiency of public transports. This paper presents an adaptive dynamic TSP control method of public traffic, by adjusting the signal time to give buses passing priority at intersections, which can take into account not only the dynamic time of bus's reaching the intersections according to the actual road conditions, but also the benefit balance of priority and non-priority vehicles. This control method has been successfully piloted in BRT Line 1 in Guangzhou, China. The average BRT vehicles' travel time under each signal priority operation is reduced by about 34 seconds, and the average delay time is decreased about 10% while it causes only acceptable negative impact on non-priority social vehicles. These pilot results demonstrate the validity and effectiveness of the control algorithm.

This report is a limited scope evaluation of the MetroRapid's first two years of operation. The MetroRapid represents Hillsborough Area Regional Transit's first foray into bus rapid transit. Built at a total cost of $34.75 million, or $1.98 million per mile, it is at the low end of the cost spectrum for BRT projects. MetroRapid includes several key BRT features such as branding of the stations and buses, increased station spacing, ticket vending machines at 12 of the 59 stations, and transit signal priority (TSP). The MetroRapid averaged 48,666 riders per month in its first two years. Total annual ridership grew 3 percent between the first and second year, which was the same growth rate as the rest of HART's fixed route system. Most passengers board and alight between Marion Transit Center and University Area Transit Center. Ridership drops off significantly between UATC and the north end of the line at Hidden River Park and Ride Lot. The MetroRapid is 10 minutes faster than the parallel Route 2. Consequently, many Route 2 customers have switched to the MetroRapid. Despite that, there has been a 10 percent net gain in transit riders on the Nebraska Avenue corridor since the MetroRapid began. Even with the TSP, traffic signal delay accounts for 21 to 24 percent of its end to end travel time. Much of the signal delay is occurring at intersections where the TSP is not activated. 17.

Mass streetcars operating record data used in a proper way can reveal the operational characteristics of streetcars to the full extent. This paper aims to design the traffic-signal progression for the modern streetcar by mining and analyzing these data. Each unit in the study consists of two adjacent signalized intersections. The k-means clustering algorithm is used to cluster the streetcar travel time data between two intersections and to determine whether the streetcar has stopped waiting for a red light at an intersection or not. An offset calculation model for streetcars is proposed to lower the impact of the conventional transit signal priority (TSP) for streetcars on motor vehicles. A real-world case study of Line One of the Nanjing, China, Qilin modern streetcar shows that the traffic-signal progression design proposed in this paper can reduce the conventional TSP’s negative impacts on motor vehicles by about 20%.

Multi-modal traffic signal control with priority, signal actuation and coordination

Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment

Public bus transit travel time is affected by many factors, including traffic signals and traffic condition. Transit agencies have implemented transit signal priority (TSP) strategies to reduce transit travel time and improve service reliability. However, due to the lack of empirical data, these factors' collective impact and bus travel time strategies have not been studied at the stop-to-stop segment level. This research focuses on the factors affecting travel time reliability, emphasising the variability between operators and the policy implications of such differences. One-way analysis of variance (ANOVA) statistical methods have been used to assess the quality implications of public bus transportation time reliability. This research seeks to investigate the factors affecting the travel time (TT) reliability of bus transport. Studies were conducted along three bus routes serving different areas. Factors strongly related to TT reliability include route length, number of signalised intersections, day of the week, bus stops, departure delays, bus lane, passenger boarding and alighting, weather condition, and fare structure. Based on the proposed model factors affecting TT reliability, it was found that TT is strongly affected by the number of bus stoppings and also the length of the route. The reliability of all three routes during the weekday is low because of delays in departure. The number of signalised intersections along the route affects reliability. Meanwhile, more passengers boarding and paying cash increased the travel time reliability of buses.