Bus priority

Abstract: Transit Signal Priority (TSP) is a tool that can be used to help make transit service more reliable, faster, and more cost effective. TSP has little impact on general traffic and is an inexpensive way to make transit more competitive with the automobile. It is used extensively in other parts of the world, and is rapidly becoming more popular in the United States. TSP is an operational strategy that facilitates the movement of transit vehicles (usually those in-service), either buses or streetcars, through traffic-signal controlled intersections. Objectives of TSP include improved schedule adherence and improved transit travel time efficiency while minimizing impacts to normal traffic operations. TSP is made up of four components. There is (1) a detection system that lets the TSP system know where the vehicle requesting signal priority is located. The detection system communicates with a (2) priority request generator that alerts the traffic control system that the vehicle would like to receive priority. There is software that processes the request and decides whether and how to grant priority based on the programmed (3) priority control strategies. And there is software that (4) manages the system, collects data, and generates reports. There are a variety of technical approaches that can be used as control strategies. This handbook provides information on the control strategies. The handbook contains the steps one should follow to implement a successful TSP project. It relies heavily on eight case studies in which a great deal of information was gathered on topics related to planning, design, implementation, evaluation, technology, institutional issues, public reaction, and much more.

Abstract: A detailed analysis of transit speeds, delays, and dwell times based on surveys conducted in a cross section of U.S. cities is summarized. The relationships and parameters provide inputs for planning service changes and assessing their impacts. The surveys and analyses find that car speeds are consistently 1.4 to 1.6 times as fast as bus speeds; time the typical bus speeds about 48 to 75 percent of its moving, 9 to 25 percent at passenger stops, and 12 to 26 percent in traffic delays; and peak-hour bus travel times approximate 4.2 min/mile in suburbs, 6.0 in the city, and 11.50 in the central business district. Bus dwell times (including door opening and closing) approximate 5 sec plus 2.75 times the number of passenger; during peak hours local buses stop at 68 to 78 percent of the designated stops. Bus travel times and speeds were derived as a function of stop frequency, stop duration, and bus acceleration and deceleration times observed in the field. Reducing bus stops from eight to six per mile and dwell times from 20 to 15 sec would reduce travel times from 6 to 4.3 min/mile, a time saving greater than that which could be achieved by eliminating traffic congestion. Transit performance should be improved by keeping the number of stoping places to a minimum. Fare-collection policies and door configurations and widths are important in reducing dwell time, especially along high-density routes. Such time savings will likely exceed those achieved from providing bus priority measures or improving traffic flow.

Abstract: Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus–Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority.

Abstract: In this paper, a person-delay-based optimization method is proposed for an intelligent TSP logic that enables bus/signal cooperation and coordination among consecutive signals under the Connected Vehicle environment. This TSP logic, called TSPCV-C, provides a method to secure the mobility benefit generated by the intelligent TSP logic along a corridor so that the bus delay saved at an upstream intersection is not wasted at downstream intersections. The problem is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound method. Minimizing per person delay has been adopted as the criterion for the model. The TSPCV-C is also designed to be conditional. That is, TSP is granted only when the bus is behind schedule and the grant of TSP causes no extra total person delay. The logic developed in this research is evaluated using both analytical and microscopic traffic simulation approaches. Both analytical tests and simulation evaluations compared four scenarios: without TSP (NTSP), conventional TSP (CTSP), TSP with Connected Vehicle (TSPCV), and Coordinated TSP with Connected Vehicle (TSPCV-C). The measures of effectiveness used include bus delay and total travel time of all travelers. The performance of TSPCV-C is compared against conventional TSP (CTSP) under four congestion levels and five intersection spacing cases. The results show that the TSPCV-C greatly reduces bus delay at signalized intersection for all congestion levels and spacing cases considered. Although the TSPCV is not as efficient as TSPCV-C, it still demonstrates sizable improvement over CTSP. An analysis on the intersection spacing cases reveals that, as long as the intersections are not too closely spaced, TSPCV can produce a delay reduction up to 59%. Nevertheless, the mechanism of TSPCV-C is recommended for intersections that are spaced less than 0.5 mile away. Simulation based evaluation results show that the TSPCV-C logic reduces the bus delay between 55% and 75% compared to the conventional TSP. The range of improvement corresponding to the four different v/c ratios tested, which are 0.5, 0.7, 0.9 and 1.0, respectively. No statistically significant negative effects are observed except when the v/c ratio equals 1.0.