TRCLC 17-11
Evaluation of Transit Priority Treatments in Tennessee
Summary:
Many big cities are progressively implementing transit friendly corridors especially in urban areas where traffic may be increasing at an alarming rate. Over the years, Transit Signal Priority (TSP) has proven to be very effective in creating transit friendly corridors with its ability to improve transit vehicle travel time, serviceability and reliability. TSP as part of Transit Oriented Development (TOD) is associated with great benefits to community livability including less environmental impacts, reduced traffic congestions, fewer vehicular accidents and shorter travel times among others. This research have therefore analyzed the impact of TSP on bus travel times, late bus recovery at bus stop level, delay (on mainline and side street) and Level of Service (LOS) at intersection level on selected corridors and intersections in Nashville Tennessee; to solve the problem of transit vehicle delay as a result of high traffic congestion in Nashville metropolitan areas. This study also developed a flow-delay model to predict delay per vehicle for a lane group under interrupted flow conditions and compared some measure of effectiveness (MOE) before and after TSP. Unconditional green extension and red truncation active priority strategies were developed via Vehicle Actuated Programing (VAP) language which was tied to VISSIM signal controller to execute priority for transit vehicles approaching the traffic signal at 75m away from the stop line. The findings from this study indicated that TSP will recover bus lateness at bus stops 25.21% to 43.1% on the average, improve bus travel time by 5.1% to 10%, increase side street delay by 15.9%, and favor other vehicles using the priority approach by 5.8% and 11.6% in travel time and delay reduction respectively. Findings also indicated that TSP may not affect LOS under low to medium traffic condition but LOS may increase under high traffic condition
Problem:
Nashville is one of the fastest growing cities in the United States, and based on the U.S census data, Nashville Metropolitan area gained 30,875 people a year between July 2010 and 2015, and according to statistics an estimate of 85 people a day come in to Nashville. This rapid growth rate is resulting to high traffic congestion especially in areas surrounded by business centers and Industries. The traffic congestion at signalized intersections is resulting to increased bus delays as a result buses are always late and are no longer attractive to road users even to automobile owners. Bus lateness is one of the major reasons why people will prefer to use their private vehicles other than patronize buses. If buses are prioritized at signalized intersections, their travel time will reduce; a reduction in bus travel time will increase the attractiveness of buses, and thus reduce auto dependency which will further lead to a reduction in traffic congestion. The corridor under study in this research is the Gallatin Pike corridor; which is a very busy bus route termed the Metropolitan Transportation Authority’s (MTA) heaviest route with over 80 000 riders per month [9]. Gallatin corridor has a total of 48 signalized intersections, and buses using this corridor experience a lot of delays at these traffic signals as a result of the traffic congestion on the corridor at peak hours. The most cost effective method to address this issue of bus delays and traffic congestions without excessive impact on road users; is the implementation of TSP. This research has simulated the Gallatin pike corridor alongside Nolensville pike in order to investigate the effectiveness of TSP on bus delay reduction and schedule adherence. This study further developed delay models to predict control delay for under saturated and saturated flow conditions in an urban motorized environment with interrupted flow under mixed traffic conditions.
Research Results:
From the corridor based analysis, it was observed that TSP will yield great benefits in bus travel time reduction, and will not only benefit buses alone but also favor other vehicle types using the bus route. The study also considered evaluating travel time reduction with different priority green time and has observed that 15 seconds of priority green time will yield greater travel time benefits compared to 10 seconds of priority green. It was also observed that travel time for buses reduced by 5.1% to 10%, while other vehicle types experienced a travel time reduction of 4.3% to 7.3%. Buses also experienced an 11.4% to 22.9% reduction in delay while delay for other vehicle types reduced by 8.9% to 14.4%. Each bus stop in the study segment was also analyzed and it was observed that TSP will recover bus lateness up to 25.21% to 43.1% on the average. It was also discovered that TSP may not benefit the crossing street traffic as they experienced an increase in delay up to 15.9%. From the analysis of isolated intersection, it was discovered that TSP reduce bus delays up to 34% to 76%, and on the average reduce delay for other vehicles on the priority approach up to 3% to 9%, while side street experienced an increase in delay up to 0.1% to 18% when signalized intersections are analyzed in isolation. Results from both scenarios show the effectiveness of TSP to reduce bus delays and improve travel times, with an improvement in bus schedule adherence. It was also observed that under medium traffic condition TSP may increase control delay but not LOS, however there may be an increase in control delay and LOS under high traffic condition.
Research developed a flow-delay and a queue-delay model under interrupted flow condition in a mixed traffic environment. The flow delay model will predict delay even under TSP conditions and the queue-delay model will predict queue length for an amount of traffic delay per vehicle; i.e. it linearizes queue length against delay for an intersection approach. It was observed that the shape of the flow-delay curve for uninterrupted flow is similar to that of the flow-delay curve for interrupted flow developed in this research. Although they follow the same pattern and shape, it should be noted that the pattern of the curve may be influenced by location of the study area, traffic volume data, vehicle composition and types, among other factors.