Impact of Traffic Congestion on Bus Travel Time in Northern New Jersey

Abstract Improving bus operation quality can attract more commuters to use bus transit, and therefore reduces the share of car and alleviates traffic congestion. One important index of bus operation quality is the bus travel time reliability, which in this paper is defined to be the probability when the sum of bus stop waiting time and in-vehicle travel time is less than a certain threshold. We formulate the bus travel time reliability by the convolution of independent events’ probabilities, and elaborate the calculation method using Automatic Vehicle Location (AVL) data. Next, the No.63 Bus Line in Harbin City is used to test the applicability of the proposed method, and analyze the influence factors of the bus travel time reliability. The numerical results show that factors such as weather, workday, departure time, travel distance, and the distance from the boarding stop to the bus departure station will significantly affect the travel time reliability. At last, some general conclusions and future research are summarized.

Download Full-text

AI for Real-Time Bus Travel Time Prediction in Traffic Congestion Management

10.1007/978-3-030-72188-6_4 ◽

2021 ◽

pp. 63-84

Author(s):

Yuming Ou

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Congestion Management ◽

Travel Time Prediction ◽

Time Prediction ◽

Bus Travel Time

Download Full-text

Contrasting Time-Based and Distance-Based Measures for Quantifying Traffic Congestion Levels: Analysis of New Jersey Counties

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1817-18 ◽

2002 ◽

Vol 1817 (1) ◽

pp. 143-148 ◽

Cited By ~ 5

Author(s):

Robert R. J. d'Abadie ◽

Theodore F. Ehrlich

Keyword(s):

New Jersey ◽

Travel Time ◽

Traffic Congestion ◽

The State ◽

Peak Period ◽

Policy Makers ◽

Capacity Ratio ◽

The Common ◽

Common Perception

Various approaches for quantifying congestion and how these different measures affect the perception of the problem are discussed. In a study done for the state of New Jersey, thresholds of the volume-capacity ratio on any given roadway were adopted to identify where congestion was occurring. The severity of this congestion was then analyzed by using both distance-based and time-based measures to describe the magnitude of the problems. It was found that the distance-based measures such as vehicle kilometers of travel indicated a relatively small amount of congestion to be present statewide. Time-based measures such as vehicle hours of travel in congestion revealed more severe problems, with more than half of total peak period travel time in many counties being spent in congested conditions. The time-based measures of congestion provided a stronger basis for more generalized conclusions. These measures indicated that much of the delay due to congestion in New Jersey could be attributed to the most severely congested locations in the state. These same time-based measures also strongly suggested that arterial roadways contribute far more to the overall congestion problem than previously reported. Time-based congestion measures provide a different perception on congestion, one in keeping with the common perception of the problem. Time-based congestion measures also provide stronger guidance on identifying major issues, enabling policy makers to better address problems within the state and solutions that are most likely to have the greatest impact.

Download Full-text

Values of Travel Time for Recreational Trips under Different Behavioural Rules

Sustainability ◽

10.3390/su13126831 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6831

Author(s):

Rosa Marina González ◽

Concepción Román ◽

Ángel Simón Marrero

Keyword(s):

Travel Time ◽

National Parks ◽

Traffic Congestion ◽

National Park ◽

Revealed Preference ◽

Discrete Choice Models ◽

Model Specification ◽

Regret Minimization ◽

Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

Download Full-text

Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2020.102920 ◽

2021 ◽

Vol 124 ◽

pp. 102920

Author(s):

Nicolas Chiabaut ◽

Rémi Faitout

Keyword(s):

Travel Time ◽

Traffic Congestion ◽

Travel Time Prediction ◽

Time Prediction

Download Full-text

Measuring Recurrent and Nonrecurrent Traffic Congestion

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1856-12 ◽

2003 ◽

Vol 1856 (1) ◽

pp. 118-124 ◽

Cited By ~ 84

Author(s):

Alexander Skabardonis ◽

Pravin Varaiya ◽

Karl F. Petty

Keyword(s):

Los Angeles ◽

Travel Time ◽

San Francisco ◽

Traffic Congestion ◽

Real Life ◽

Time Variability ◽

Loop Detectors ◽

Bay Area ◽

Freeway Corridors ◽

Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

Download Full-text

Road Traffic Congestion Management Based on a Search-Allocation Approach

Transport and Telecommunication Journal ◽

10.1515/ttj-2017-0003 ◽

2017 ◽

Vol 18 (1) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Jamal Raiyn

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Traffic Management ◽

Road Traffic ◽

Smart Cities ◽

Self Regulation ◽

Congestion Management ◽

Shortest Route ◽

Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

Download Full-text

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Computational Intelligence and Neuroscience ◽

10.1155/2015/432389 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 31

Author(s):

Cong Bai ◽

Zhong-Ren Peng ◽

Qing-Chang Lu ◽

Jian Sun

Keyword(s):

Travel Time ◽

Dynamic Model ◽

Kalman Filtering ◽

Prediction Models ◽

Support Vector ◽

Travel Times ◽

Travel Time Prediction ◽

Real World Data ◽

Time Prediction ◽

Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Download Full-text