Buses as a Traffic Probe: Demonstration Project

Author(s):  
Randolph W. Hall ◽  
Nilesh Vyas

The congestion probe feature of the Orange County Transportation Authority (California) bus probe project was evaluated by comparing automobile and bus trajectories and examining alternative congestion detection methods. The focus was city streets on which delays occur at signalized intersections and bus delays at bus stops. The analysis revealed that when automobiles have long delays, buses traveling nearby on the same route are also likely to be delayed. The reverse situation, however, is not always true, because buses frequently wait for extended periods when they run ahead of schedule. Any useful bus probe algorithm needs to distinguish between actual congestion and a stopping delay. Although the transit probe was designed to measure congestion on roadway segments, a more useful approach would be to measure congestion approaching major intersections, where delays are likely to occur. Moreover, because delays randomly fluctuate according to a vehicle’s arrival time relative to the signal cycle, the most sensible approach is to set off a "congestion alarm" when a vehicle is delayed by more than one cycle at an intersection. A congestion alarm would indicate oversaturation and delay well above normal.

Author(s):  
B. Anbaroglu ◽  
B. Heydecker ◽  
T. Cheng

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.


2014 ◽  
Vol 68 ◽  
pp. 123-140 ◽  
Author(s):  
Weihua Gu ◽  
Vikash V. Gayah ◽  
Michael J. Cassidy ◽  
Nathalie Saade

2019 ◽  
Vol 9 (4) ◽  
pp. 4574-4580
Author(s):  
S. A. Arhin ◽  
A. Gatiba ◽  
M. Anderson ◽  
B. Manandhar ◽  
M. Ribbisso

This study aimed at determining patrons’ acceptable wait times beyond the bus scheduled arrival time at bus stops in Washington, DC and to develop accompanying prediction models to provide decision-makers with additional tools to improve patronage. The research primarily relied on a combination of manual and video-based data collection efforts. Manual field data collection was used for surveying patrons to obtain their suggested acceptable wait times at bus stops, while video-based data collection was used to obtain bus stop characteristics and operations. In all, 3,388 bus patrons at 71 selected bus stops were surveyed. Also, operational data for 2,070 bus arrival events on 226 routes were extracted via video playback. Data were collected for AM peak, PM peak and mid-day periods of nine-month duration from May 2018 through January 2019. The results of the survey showed that the minimum acceptable wait time beyond the scheduled arrival time was reported to be 1 minute, while the maximum acceptable wait time was reported to be 20 minutes. Regression analyses were conducted to develop models to predict the maximum acceptable wait time based on factors including temperature, presence of shelter at the bus stops, average headway of buses, and patrons’ knowledge of bus arrival times. The models were developed for A.M., P.M. and mid-day periods. The F-Statistics for the models were determined to be statistically significant with p values


for most city travelers, the arrival time of busses is the key detail. Excessively long waiting times at bus stops also deter and make relevant travelers from taking buses. In this paper, a method of bus arrival time prediction is presented based on participatory passenger sensing.With commodity cell phones,t he local environmental history of bus passengers is effectively gathered and used to predict bus travel routes and forecast bus arrival times at different bus stops. The proposed program relies entirely on the participating users 'joint efforts and is independent of the bus operating companies, In this way, universal bus service support can be effectively introduced without the need for funding from different bus operating companies. The resort to more commonly accessible and energy-e fficient sensing devices, including cell tower signals, movement st atuses, audio recordings, etc., instead of referring to location information permitted by GPS,Which puts less pressure on the in volved party and encourages its involvement. A prototype system is designed with various types of Android based cell phones and an extensive trial duration of 7 weeks with the NTU campus shuttle buses as well as Singapore city buses. The test results indicate that the program proposed achieves excellent predictive accuracy compared to the solutions implemented by certain bus operators and supported by GPS. Further implementing the system and conducting 4-day rapid trials with London bus system, indicating quick implementation o f the proposed system and promising city-wide results. At the same time, the proposed solution is available in more general terms and is energy efficient.


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