Transit Signal Priority in Smart Cities

Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations

A Modular, Adaptive, and Autonomous Transit System (MAATS): A In-motion Transfer Strategy and Performance Evaluation in Urban Grid Transit Networks

Dynamic traffic demand has been a longstanding challenge for the conventional transit system design and operation. The recent development of autonomous vehicles (AVs) makes it increasingly realistic to develop the next generation of transportation systems with the potential to improve operational performance and flexibility. In this study, we propose an innovative transit system with autonomous modular buses (AMBs) that is adaptive to dynamic traffic demands and not restricted to fixed routes and timetables. A unique transfer operation, termed as “in-motion transfer”, is introduced in this paper to transfer passengers between coupled modular buses in motion. A two-stage model is developed to facilitate in-motion transfer operations in optimally designing passenger transfer plans and AMB trajectories at intersections. In the proposed AMB system, all passengers can travel in the shortest path smoothly without having to actually alight and transfer between different bus lines. Numerical experiments demonstrate that the proposed transit system results in shorter travel time and a significantly reduced average number of transfers. While enjoying the above-mentioned benefits, the modular, adaptive, and autonomous transit system (MAATS) does not impose substantially higher energy consumption in comparison to the conventional bus syste

Arterial traffic signal optimization: a person-based approach

This paper presents a real-time signal control system that optimizes signal settings based on minimization of person delay on arterials. The system’s underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays associated with interruptions of platoons. In addition to the fact that it utilizes readily available technologies to obtain the inputs for the optimization, the system’s feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system is tested on a four-intersection segment of San Pablo Avenue arterial located in Berkeley, California. The findings show the system’s capability to outperform pretimed (i.e., fixed-time) optimal signal settings by reducing total person delay. They have also demonstrated its success in reducing bus person delay by efficiently providing priority to transit vehicles even when they travel in conflicting directions

Person-based Adaptive Priority Signal Control with Connected-vehicle Information

This thesis proposes a TSP (transit signal priority) strategy of person-based adaptive priority signal control with connected-vehicle information (PAPSCCI). By minimizing the total person delay at an isolated intersection, PAPSCCI can assign signal priorities to transit vehicles due to their high occupancies, while minimize the negative impact to the auto traffic. With the accurate vehicle information provided by connected-vehicle technology, PAPSCCI can estimate person delay for each passenger directly and form a MILP (mixed-integer linear program) for the optimization. Performances of PAPSCCI were evaluated through simulations. Results show decreases of both vehicle delay and person delay of all vehicle types when there are up to three bus routes running through the intersection. How different penetration rates of the connected-vehicle technology affect the performance of the PAPSCCI were tested. Necessary revisions were made to the PAPSCCI model considering different penetration rates. Results show that the effectiveness of PAPSCCI worsens with the lowering of penetration rate. The delay improvements, however, were still promising when the penetration rate is above 40%. PAPSCCI model were also developed and tested with communication range of 2000 m, 1000 m, 500 m and 250 m. Expect that the 1000 m case has the best delay improvements after PAPSCCI optimization, the effectiveness of the model worsens when the communication range getting smaller. Even when the communication range is down to 250 m, PAPSCCI can still reduce the delay for all vehicle types

Development and Evaluation of An Adaptive Transit Signal Priority System Using Connected Vehicle Technology

Transit signal priority (TSP) can be a very effective preferential treatment for transit vehicles in congested urban networks. There are two problems with the current practice of the transit signal priority. First, random bus arrival time is not sufficiently accounted for, which’ve become the major hindrance in practice for implementing active or adaptive TSP strategies when a near-side bus stop is present. Secondly, most research focuses on providing bus priority at local intersection level, but bus schedule reliability should be achieved at route level and relevant studies have been lacking. In the first part of this research, a stochastic mixed-integer nonlinear programming (SMINP) model is developed to explicitly to account for uncertain bus arrival time. A queue delay algorithm is developed as the supporting algorithm for SMINP to capture the delays caused by the interactions between vehicle queues and buses entering and exiting near-side bus stops. A concept of using signal timing deviations to approximate the impacts of TSP operations on other traffic is proposed for the first time in this research. In the second part of the research, the deterministic version of the SMINP model is extended to the arterial setting, where a route-based TSP (R-TSP) model is develop to optimize for schedule-related bus performances on the corridor level. The R-TSP model uses the real-time data available only from the connected vehicle communications technology. Based on the connected vehicle technology, a real-time signal control system that implements the proposed TSP models is prototyped in the simulation environment. The connected vehicle technology is also used as the main detection and monitoring mechanism for the real-time control of the adaptive TSP signal system. The adaptive TSP control module is designed as a plug-in module that is envisioned to work with a modern fixed-time or adaptive signal controller with connected vehicle communications capabilities. Using this TSP-enabled signal control system, simulation studies were carried out in both a single intersection setting and a five-intersection arterial setting. The effectiveness of the SMINP model to handle uncertain bus arrival time and the R-TSP model to achieve corridor-level bus schedule reliability were studied. Discussions, conclusions and future research on the topic of adaptive TSP models were made