Performance of Transit Signal Priority with Queue Jumper Lanes

2005 ◽  
Vol 1925 (1) ◽  
pp. 265-271 ◽  
Author(s):  
Guangwei Zhou ◽  
Albert Gan
Keyword(s):  
Transit Signal Priority ◽  
Preferential Treatment ◽  
Local Conditions ◽  
Bus Stop ◽  
Optimal Detector ◽  
Bus Delay ◽  
Traffic Volumes ◽  
Bus Stops ◽  
Detector Location

Queue jumper lanes are a special type of bus preferential treatment that allows buses to bypass a waiting queue through a right-turn bay and then cut out in front of the queue by getting an early green signal. The performance of queue jumper lanes is evaluated under different transit signal priority (TSP) strategies, traffic volumes, bus volumes, dwell times, and bus stop and detector locations. Four TSP strategies are considered: green extension, red truncation, phase skip, and phase insertion. It was found that queue jumper lanes without TSP were ineffective in reducing bus delay. Queue jumper lanes with TSP strategies that include a phase insertion were found to be more effective in reducing bus delay while also improving general vehicle operations than those strategies that do not include this treatment. Nearside bus stops upstream of check-in detectors were preferred for jumper TSP over farside bus stops and nearside bus stops downstream of check-in detectors. Through vehicles on the bus approach were found to have only a slight impact on bus delay when the volume-to-capacity (v/c) ratio was below 0.9. However, when v/c exceeded 0.9, bus delay increased quickly. Right-turn volumes were found to have an insignificant impact on average bus delay, and an optimal detector location that minimizes bus delay under local conditions was shown to exist.

Estimating the Impacts of Bus Stops and Transit Signal Priority on Intersection Operations: Queuing and Variational Theory Approach

2017 ◽  
Vol 2622 (1) ◽  
pp. 70-83 ◽  
Author(s):  
Kan Wu ◽  
S. Ilgin Guler ◽  
Vikash V. Gayah
Keyword(s):  
Queuing Theory ◽  
Theory Approach ◽  
Transit Signal Priority ◽  
Variational Theory ◽  
Traffic Conditions ◽  
Bus Stop ◽  
Car Traffic ◽  
Trade Offs ◽  
Bus Delay ◽  
Local Agencies

Transit signal priority (TSP) can be used to improve bus operations at signalized intersections, often to the detriment of general car traffic. However, the impacts of TSP treatments applied to intersections with nearby bus stop locations are currently unknown. This paper quantifies changes in intersection capacity, car delay, and bus delay when priority is provided to buses that dwell at near- or farside bus stop locations through green extension or red truncation. Variational and kinematic wave theories are used to estimate car capacity and bus delay for oversaturated traffic conditions; queuing theory is used to estimate car and bus delays for undersaturated conditions. Numerical analyses are conducted to explore the impacts on various bus stop locations and bus dwell time durations. These results illustrate clear trade-offs between reduced bus delays and increased car delays or reduced intersection capacities that can be quantified with the proposed method. The results also reveal that the effects of TSP vary dramatically with bus dwell times for a given bus stop location. The proposed method and associated results can be used to implement TSP strategies to meet the specific needs of local agencies.

Improved Transit Signal Priority System for Networks with Nearside Bus Stops

2005 ◽  
Vol 1925 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Wonho Kim ◽  
L. R. Rilett
Keyword(s):  
Dwell Time ◽  
Prediction Interval ◽  
Weighted Least Squares ◽  
Least Squares Regression ◽  
Transit Signal Priority ◽  
Bus Stop ◽  
Bus Stops ◽  
Negative Impacts ◽  
In Transit ◽  
Signal Operations

Transit signal priority (TSP), which has been deployed in many cities in North America and Europe, is a traffic signal enhancement strategy that facilitates efficient movement of transit vehicles through signalized intersections. Most TSP systems, however, do not work well in transit networks with nearside bus stops because of the uncertainty in bus dwell time. Unfortunately, most bus stops on U.S. arterial roadways are nearside ones. In this research, weighted-least-squares regression modeling was used to estimate bus stop dwell time and, more important, the associated prediction interval. An improved TSP algorithm that explicitly considers the prediction interval was developed to reduce the negative impacts of nearside bus stops. The proposed TSP algorithm was tested on a VISSIM model of an urban arterial section of Bellaire Boulevard in Houston, Texas. In general, it was found that the proposed TSP algorithm was more effective than other algorithms because it improved bus operations without statistically significant impacts on signal operations.

Modeling Bus Capacity for Bus Stops Using Queuing Theory and Diffusion Approximation

2021 ◽  
pp. 036119812110302
Author(s):  
Chao Wang ◽  
Weijie Chen ◽  
Yueru Xu ◽  
Zhirui Ye
Keyword(s):  
Approximation Method ◽  
Service Time ◽  
Diffusion Approximation ◽  
Coefficient Of Variation ◽  
Queuing Theory ◽  
Queuing Model ◽  
Bus Stop ◽  
Proposed Model ◽  
Bus Stops ◽  
Bus Service

For bus service quality and line capacity, one critical influencing factor is bus stop capacity. This paper proposes a bus capacity estimation method incorporating diffusion approximation and queuing theory for individual bus stops. A concurrent queuing system between public transportation vehicles and passengers can be used to describe the scenario of a bus stop. For most of the queuing systems, the explicit distributions of basic characteristics (e.g., waiting time, queue length, and busy period) are difficult to obtain. Therefore, the diffusion approximation method was introduced to deal with this theoretical gap in this study. In this method, a continuous diffusion process was applied to estimate the discrete queuing process. The proposed model was validated using relevant data from seven bus stops. As a comparison, two common methods— Highway Capacity Manual (HCM) formula and M/M/S queuing model (i.e., Poisson arrivals, exponential distribution for bus service time, and S number of berths)—were used to estimate the capacity of the bus stop. The mean absolute percentage error (MAPE) of the diffusion approximation method is 7.12%, while the MAPEs of the HCM method and M/M/S queuing model are 16.53% and 10.23%, respectively. Therefore, the proposed model is more accurate and reliable than the others. In addition, the influences of traffic intensity, bus arrival rate, coefficient of variation of bus arrival headway, service time, coefficient of variation of service time, and the number of bus berths on the capacity of bus stops are explored by sensitivity analyses.

Study on the Optimal Improvement of the Bus Station

2012 ◽  
Vol 253-255 ◽  
pp. 1776-1781
Author(s):  
Wen Hua Jiang ◽  
Xian Xiang Wang ◽  
Hang Fei Lin
Keyword(s):  
Site Location ◽  
Bus Stop ◽  
Site Layout ◽  
Urban Bus ◽  
Bus Station ◽  
Bus Stops

Starting from several aspects of site location, site size and site layout, this document studies the urban bus stop systematically, proposes the setting principles of urban bus stop. Take Yiwu bus stops for example, which focus on the analysis of the reasonable setting of the sites, and has provided guidance for the layout of urban bus stop.

Optimizing School Bus Stop Placement in Howard County, Maryland

2016 ◽  
pp. 1660-1676 ◽  
Author(s):  
Michael Galdi ◽  
Paporn Thebpanya
Keyword(s):  
Travel Time ◽  
Carbon Footprint ◽  
Current System ◽  
School Bus ◽  
School Officials ◽  
Statistical Estimates ◽  
Bus Stop ◽  
Network Analyst ◽  
Bus Stops ◽  
State And Local

In the current system, school bus stops in Howard County, Maryland are manually placed along the school bus routes based on safety, cost-efficiency, and many other variables. With such liberal placement, bus stops are sometimes placed unnecessarily. This issue is prevalent in many school districts and often results in needlessly close bus stop proximity. In this study, the authors implemented a GIS-based heuristic to assist school officials in optimizing their districts bus stop placement. They also estimated the proportion of county-wide bus stops that could be eliminated by this approach. Following the constraints determined by State and local guidelines, the ArcGIS Network Analyst Extension was used to identify unnecessary bus stops across the study area. The initial output was re-evaluated by school officials in order to determine if those bus stops would be eliminated. The results indicate that approximately 30% of the existing bus stops were marked as “candidates for elimination” by the GIS process. After a review of these candidates, it was determined that at least 15% of the total school bus stops could be eliminated. Statistical estimates lent credence to the benefit of a re-evaluation of these bus stops. The method developed in this study can easily be replicated. Hence, it may inspire other school systems to exercise the same approach. Additionally, the results provide a gateway for future studies in examining more efficient school bus routes with less travel time, as well as investigating how much the carbon footprint of school bus fleets can be reduced.

Does Combining Transit Signal Priority with Dedicated Bus Lanes or Queue Jump Lanes at Multiple Intersections Create Multiplier Effects?

2017 ◽  
Vol 2647 (1) ◽  
pp. 80-92 ◽  
Author(s):  
Long T. Truong ◽  
Graham Currie ◽  
Mark Wallace ◽  
Chris De Gruyter
Keyword(s):  
Public Transport ◽  
Policy Implication ◽  
Transit Signal Priority ◽  
Time And Space ◽  
Multiplier Effect ◽  
Bus Delay ◽  
Simulation Results ◽  
Multiplier Effects ◽  
Extensive Body ◽  
Multiple Intersections

An extensive body of literature deals with the design and operation of public transport (PT) priority measures. However, there is a need to understand whether providing transit signal priority with dedicated bus lanes (TSPwDBL) or transit signal priority with queue jump lanes (TSPwQJL) at multiple intersections creates a multiplier effect on PT benefits. If the benefit from providing priority together at multiple intersections is greater than the sum of benefits from providing priority separately at each of those individual intersections, a multiplier effect exists. This paper explores the effects of providing TSPwDBL or TSPwQJL at multiple intersections on bus delay savings and person delay savings. Simulation results reveal that providing TSPwDBL or TSPwQJL at multiple intersections may create a multiplier effect on one-directional bus delay savings, particularly when signal offsets provide bus progression for that direction. The multiplier effect may result in a 5% to 8% increase in bus delay savings for each additional intersection with TSPwDBL or TSPwQJL. A possible explanation is that TSPwDBL and TSPwQJL can reduce the variations in bus travel times and thus allow signal offsets—which account for bus progression—to perform even better. Furthermore, results show little evidence of the existence of a multiplier effect on person delay savings, particularly for TSPwQJL with offsets that favor person delay savings. A policy implication of these findings is that considerable PT benefits can be achieved by providing both time and space priority in combination on a corridorwide scale.

scholarly journals A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location

2019 ◽  
Vol 11 (4) ◽  
pp. 97 ◽  
Author(s):  
Peixin Dong ◽  
Dongyuan Li ◽  
Jianping Xing ◽  
Haohui Duan ◽  
Yong Wu
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Network Optimization ◽  
Original Network ◽  
Network Efficiency ◽  
Bus Stop ◽  
Time Performance ◽  
Network Time ◽  
Vehicle Location ◽  
Bus Stops

Aiming at the problems of poor time performance and accuracy in bus stops network optimization, this paper proposes an algorithm based on complex network and graph theory and Beidou Vehicle Location to measure the importance of bus stops. This method narrows the scope of points and edges to be optimized and is applied to the Jinan bus stop network. In this method, the bus driving efficiency, which can objectively reflect actual road conditions, is taken as the weight of the connecting edges in the network, and the network is optimized through the network efficiency. The experimental results show that, compared with the original network, the optimized network time performance is good and the optimized network bus driving efficiency is improved.

scholarly journals Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Vee-Liem Saw ◽  
Luca Vismara ◽  
Lock Yue Chew
Keyword(s):  
Reinforcement Learning ◽  
Phase Difference ◽  
Waiting Time ◽  
Learning Process ◽  
Average Waiting Time ◽  
Bus Stop ◽  
Emergent Behaviour ◽  
Reasonable Amount ◽  
Bus Stops ◽  
Simulation Time

We study how N intelligent buses serving a loop of M bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. A reward that encourages the buses to strive towards a staggered phase difference amongst them whilst picking up passengers allows the reinforcement learning process to converge to an optimal Q-table within a reasonable amount of simulation time. It is remarkable that this emergent behaviour of intelligent buses turns out to minimise the average waiting time of commuters, in various setups where buses move with the same speed or different speeds, during busy as well as lull periods. Cooperative actions are also observed, e.g., the buses learn to unbunch.

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