Diagnosing Obstacles to Speed and Reliability with High-Resolution Automatic Vehicle Locator Data: Bus Time Budgets

2021 ◽  
pp. 036119812110299
Author(s):  
Eric Lind ◽  
Joseph Reid
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Time Budget ◽  
Traffic Signals ◽  
Signal Delay ◽  
Agency Staff ◽  
Bus Service ◽  
Transit Agencies ◽  
Improve Service Quality ◽  
Vehicle Travel Time

Transit riders consistently rate speed and reliability of service as primary drivers of satisfaction, and transit agencies can help retain and grow ridership by improving these components of service. The challenge for transit agency staff is to identify when and where they should focus efforts to improve service quality. Here we propose an approach to data analysis that identifies and isolates specific aspects of service that are limiting speed and reliability. In-vehicle travel time can be decomposed into time spent in motion and time stopped. Time in motion is often dependent on factors common to general traffic, whereas time stopped has some features in common with general traffic (i.e., traffic signals) and some unique to buses (i.e., passenger dwell). Other sources of delay from serving a bus stop include deceleration, acceleration, and signal delay. To improve overall travel time, transit agencies must prioritize interventions that will contribute the most to improving speed and reliability. We used high-resolution automatic vehicle locator data to assign components of speed and reliability within a trip-level “time budget.” We compared typical time budget components across service types, and used the time budget approach to evaluate local service and Rapid bus service operating simultaneously on the same alignment. Results of the delay and variability quantifications suggested particular interventions, as well as the expected size of the resulting effect. With limited resources, the bus time budget approach could aid understanding and prioritization of transit agency efforts to improve speed and reliability.

Comparison of Simulation Modules of TRANSYT and INTEGRATION Models

1996 ◽  
Vol 1566 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Hesham A. Rakha ◽  
Michel W. Van Aerde
Keyword(s):  
Travel Time ◽  
Cycle Length ◽  
Traffic Signals ◽  
Complex Signal ◽  
Dynamic Features ◽  
Total Travel Time ◽  
Vehicle Delay ◽  
The Difference ◽  
Vehicle Travel Time ◽  
Signal Network

The TRANSYT simulation/optimization model serves as an unofficial international standard against which many measure the efficiency of other methods of coordinating networks of traffic signals that operate at a constant and common cycle length. However, dynamics due to traffic rerouting, the simultaneous operation of adjacent traffic signals at different cycle lengths, the effect of queue spillbacks on the capacity of upstream links, and various forms of real-time intersection control cannot be modeled using a static model such as TRANSYT. This has created a unique niche for a more dynamic signal network simulation tool. Before modeling such special dynamic scenarios, there first exists a need to validate the static signal control features of such a model and to determine if its unique dynamic features still permit it to yield credible static results. This study has two objectives. First, it attempts to illustrate the extent to which estimates of vehicle travel time, vehicle delay, and number of vehicle stops are related when a standard static signal network is examined using both TRANSYT and INTEGRATION. Second, it strives to illustrate that the types of more complex signal timing problems, which at present cannot be examined by the TRANSYT model, can be examined using the dynamic features of INTEGRATION. The results are intended to permit a better appreciation of both their differences and similarities and permit a more informed decision as to when and where each model should be used. Also demonstrated is that INTEGRATION simulates traffic-signalized networks in a manner that is consistent with TRANSYT for conditions in which TRANSYT is valid. Specifically, the difference in total travel time and percentage of vehicle stops is within 5 percent. In addition, it is also shown that INTEGRATION can simulate conditions that represent the limitations to the current TRANSYT model, such as degrees of saturation in excess of 95 percent and adjacent signals operating at different cycle length durations. This analysis of the simulation features of TRANSYT and INTEGRATION is intended to be a precursor to a comparison of their respective optimization routines.

Using GTFS to Calculate Travel Time Savings Potential of Bus Preferential Treatments

2021 ◽  
pp. 036119812110092
Author(s):  
Daniel Arias ◽  
Kara Todd ◽  
Jennifer Krieger ◽  
Spencer Maddox ◽  
Pearse Haley ◽  
...  
Keyword(s):  
Travel Time ◽  
Planning Process ◽  
Operating Cost ◽  
Flow Characteristics ◽  
Travel Time Savings ◽  
Bus Lane ◽  
Time Savings ◽  
Bus Network ◽  
Bus Networks ◽  
Transit Agencies

Dedicated bus lanes and other transit priority treatments are a cost-effective way to improve transit speed and reliability. However, creating a bus lane can be a contentious process; it requires justification to the public and frequently entails competition for federal grants. In addition, more complex bus networks are likely to have unknown locations where transit priority infrastructure would provide high value to riders. This analysis presents a methodology for estimating the value of bus preferential treatments for all segments of a given bus network. It calculates the passenger-weighted travel time savings potential for each inter-stop segment based on schedule padding. The input data, ridership data, and General Transit Feed Specification (GTFS) trip-stop data are universally accessible to transit agencies. This study examines the 2018 Metropolitan Atlanta Rapid Transit Authority (MARTA) bus network and identifies a portion of route 39 on Buford Highway as an example candidate for a bus lane corridor. The results are used to evaluate the value of time savings to passengers, operating cost savings to the agency, and other benefits that would result from implementing bus lanes on Buford Highway. This study does not extend to estimating the cost of transit priority infrastructure or recommending locations based on traffic flow characteristics. However, it does provide a reproducible methodology to estimate the value of transit priority treatments, and it identifies locations with high value, all using data that are readily available to transit agencies. Conducting this analysis provides a foundation for beginning the planning process for transit priority infrastructure.

San Pablo, California, Corridor Rapid Service

2017 ◽  
Vol 2647 (1) ◽  
pp. 17-25
Author(s):  
Peter Martin ◽  
Nathan Landau
Keyword(s):  
Travel Time ◽  
Lessons Learned ◽  
Bus Rapid Transit ◽  
Transit Signal Priority ◽  
Rapid Transit ◽  
East Bay ◽  
Years Of Service ◽  
Bus Service

The San Pablo, California, Rapid bus service was planned 17 years ago and was implemented 13 years ago. The Rapid service, which did not include exclusive lanes, was an upgrade of previous limited-stop bus service linking the East Bay communities of San Pablo, Richmond, El Cerrito, Albany, Berkeley, Emeryville, and Oakland. The 13 years of service provide some lessons for other communities that are considering moderate (or less than full) service upgrades to bus rapid transit. The service was quick to implement and low in cost, but it has not provided the anticipated ridership benefits. The upgrades apparently were not significant enough to attract ridership increases. The transit signal priority element was not well maintained and thus has not provided the desired travel time and reliability benefits. AC Transit—which operates the service—and the corridor communities are currently reexamining further upgrades to the service. This Rapid service is well used, but more pronounced improvements are needed to fulfill ridership potential in the corridor. The lessons learned are that minor upgrades can be easily implemented, but noticeable changes are required to achieve significant ridership gains.

scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Ordinary Least Squares ◽  
Gap Filling ◽  
Service Levels ◽  
Transit Service ◽  
Logistic Regressions ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

2018 ◽  
Vol 2672 (47) ◽  
pp. 125-134 ◽  
Author(s):  
Jiayu Zhong ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Rapid Development ◽  
Influential Factors ◽  
Travel Mode ◽  
Explanatory Variables ◽  
Bus Travel Time ◽  
Vehicle Travel Time

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

Freight Vehicle Travel Time Analysis: A Case Study of Birgunj Nagdhunga Corridor

2021 ◽  
Vol 08 (02) ◽  
pp. 1-13
Author(s):  
Abhishek Jha ◽  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
License Plate ◽  
Monitoring Method ◽  
Factors Affecting ◽  
Average Travel Time ◽  
Major Factors ◽  
Vehicle Travel Time ◽  
Freight Vehicles

This study covers the freight vehicle, which clears the custom clearance process for Kathmandu and transports the same goods to Kathmandu from Birgunj. In this study average travel time for freight vehicles from Birgunj to Nagdhunga has been studied, along with the factors affecting the travel time from Birgunj to Nagdhunga. License plate monitoring method of the freight vehicles was done to find the average travel time and a questionnaire survey was done to identify the factors affecting travel time of the freight vehicle. The travel time from Birgunj to Nagdhunga is different for different types of, vehicle and good. The fastest average travel time is of fixed container of 40 feet size with 23.2 hours and longest average time is for fixed container of 20 feet size with 28.95 hours. The average travel time for non-degradable goods is 26.5 hours and for degradable goods is 22.38 hours. Major factors affecting the travel time are traffic congestion along the route, bad road condition along the route and hilly road with sharp bends, turns and grade.

A Parallel Metaheuristic Approach to Reduce Vehicle Travel Time for Smart Cities Sustainability

2021 ◽  
pp. 37-49
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart Cities ◽  
Vehicle Travel Time

scholarly journals Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

2020 ◽  
Vol 12 (9) ◽  
pp. 3863 ◽  
Author(s):  
Gamal Eldeeb ◽  
Moataz Mohamed
Keyword(s):  
Real Time ◽  
Choice Model ◽  
Information Provision ◽  
Travel Behaviour ◽  
Error Component ◽  
Transit Market ◽  
Bus Service ◽  
Transit Agencies ◽  
Real Time Information ◽  
Time Information

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

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