Dense network traffic models, travel time reliability and traffic management. Ii: Application to network reliability

1999 ◽  
Vol 33 (2) ◽  
pp. 235-251 ◽  
Author(s):  
Michael A P Taylor
Keyword(s):  
Travel Time ◽  
Network Traffic ◽  
Traffic Management ◽  
Network Reliability ◽  
Travel Time Reliability ◽  
Dense Network ◽  
Traffic Models

scholarly journals Travel Time Reliability for Urban Networks: Modelling and Empirics

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Fangfang Zheng ◽  
Xiaobo Liu ◽  
Henk van Zuylen ◽  
Jie Li ◽  
Chao Lu
Keyword(s):  
Standard Deviation ◽  
Travel Time ◽  
Network Traffic ◽  
Traffic Management ◽  
Weighted Average ◽  
Network Density ◽  
Travel Time Reliability ◽  
Time Rate ◽  
Traffic Characteristics ◽  
Average Travel Time

The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.

scholarly journals Passenger Travel Time Reliability for Multimodal Public Transport Journeys

2019 ◽  
Vol 2673 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Malvika Dixit ◽  
Ties Brands ◽  
Niels van Oort ◽  
Oded Cats ◽  
Serge Hoogendoorn
Keyword(s):  
Travel Time ◽  
Network Reliability ◽  
Real Life ◽  
Travel Time Reliability ◽  
Behavioral Models ◽  
Buffer Time ◽  
Passenger Travel ◽  
Departure Time Choice ◽  
Multiple Routes ◽  
The Impact

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.

scholarly journals Illegal parking road recognition based on video detection equipment

2018 ◽  
Vol 232 ◽  
pp. 02055
Author(s):  
Xinyue Fan ◽  
Qi Shen ◽  
Qinglong He
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Adaptive Method ◽  
Calculation Model ◽  
Descent Method ◽  
Steepest Descent Method ◽  
Travel Time Reliability ◽  
Video Detection ◽  
Actual Use ◽  
Network Status

Based on the big data collected by the video detection equipment, the network topology table of the city level video detection equipment is constructed by using the time relation and the spatial position relation of the data. By using the steepest descent method and adaptive method, the travel confidence time randomness model is constructed, which can describe whether a traveler can finish his travel time on time. It overcomes the shortcomings of the existing travel time reliability calculation model, which is difficult to combine with the actual use of video detection equipment data, then examples analysis are followed. The results show that, for the data collected by the video detection device, the travel confidence time randomness model is more accurate than the existing models. It can describe the probability of the traveler arriving at the destination in a given time more accurately, which can be used to identify illegal parking road and provide a reliable basis for traffic management departments in traffic planning, dividing road network status and traffic situation prediction.

Evaluation of Operational Effects of I-66 Active Traffic Management System

2017 ◽  
Vol 2616 (1) ◽  
pp. 91-103 ◽  
Author(s):  
PilJin Chun ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Management System ◽  
Travel Time Reliability ◽  
Variable Speed ◽  
Time Data ◽  
Speed Limits ◽  
Variable Speed Limits ◽  
Traffic Management System ◽  
Virginia Department

In September 2015, the Virginia Department of Transportation instituted an active traffic management system on I-66 in Northern Virginia. I-66 is a major commuter route into Washington, D.C., that experiences significant recurring and nonrecurring congestion. The active traffic management system sought to manage existing capacity dynamically and more effectively with hard shoulder running, advisory variable speed limits, lane use control signs, and queue warning systems. An initial before-and-after analysis of the system’s operational effectiveness was performed with probe-based travel time data from the provider, INRIX, and used records from the active traffic management’s traffic operations center. On weekdays, statistically significant improvements were often observed during off-peak periods, but conditions did not improve during peak periods. Weekends showed the greatest improvements, with travel times and travel time reliability measures improving by 10% to 14%. Segment-level analysis revealed that most of the benefits were attained because of the use of hard shoulder running outside of the peak periods, which created additional capacity on I-66. Benefits due to advisory variable speed limits were inconclusive because of limited data.

Evaluating the Impact of Real-Time Mobility and Travel Time Reliability Information on Truck Drivers’ Routing Decisions

2018 ◽  
Vol 2672 (9) ◽  
pp. 164-172 ◽  
Author(s):  
Xiaoqiang Kong ◽  
William L. Eisele ◽  
Yunlong Zhang ◽  
Daren B. H. Cline
Keyword(s):  
Decision Making ◽  
Travel Time ◽  
Real Time ◽  
Traffic Management ◽  
Truck Drivers ◽  
Decision Making Process ◽  
Travel Time Reliability ◽  
Travel Time Prediction ◽  
Binary Logistic Regression Model ◽  
The Impact

This study represents the first research to investigate the impacts of two critical determinants—level of congestion and travel time reliability—on routing decisions with two groups of truck drivers having different levels of awareness of the real-time and the historical traffic conditions on available routes. The research analyzed 14,538 global positioning system devices recording trips on the I-495 crossing through Maryland, Virginia, and Washington, DC, and 2,166 trips in the Dallas area, to explore how truck drivers make routing decisions based on real-time travel time and reliability information by applying a binary logistic regression model. Researchers found that for truck drivers who are not familiar with the historical traffic and travel time conditions on available routes, real-time congestion information is a significant factor in their routing decision-making process, while travel time reliability is not a major consideration. For frequent truck drivers who are familiar with the historical traffic and travel time conditions on available routes, travel time reliability is a significant factor in their routing decision-making process, and traffic congestion information is not a significant factor. These results bring more accuracy to travel time prediction and provide valuable insights into traffic management and reliability performance measures. Moreover, this research provides statistical evidence proving the potential value of delivering travel time reliability information to drivers, traffic management agencies, and navigation map developers.

The Network Traffic Management Based on Queuing Theory

2011 ◽  
Vol 121-126 ◽  
pp. 4721-4725
Author(s):  
Jian Ping Wang ◽  
Xiao Min Li ◽  
Cui Ling Jiao
Keyword(s):  
Network Traffic ◽  
Traffic Management ◽  
Queuing Theory ◽  
Network Reliability ◽  
Traffic Forecasting ◽  
General Network

Traffic management can improve QoS of the network, the exist well-known algorithm for the network traffic management has some problems in the sensitive degree and the network reliability, using queuing theory build up the basic M/M/1 model for the network traffic management, obtaining the network traffic forecasting ways and the stable congestion rate formula, combining the general network traffic monitor parameters, realizing the estimation and monition process for the network traffic rationally. By testing the algorithm, we know that queuing theory can optimize the network traffic, it’s convenient and simple for calculating and monitoring the network traffic properly.

Development of Decision Support System for Integrated Active Traffic Management Systems Considering Travel Time Reliability

2020 ◽  
Vol 2674 (2) ◽  
pp. 167-180 ◽  
Author(s):  
Whoibin Chung ◽  
Mohamed Abdel-Aty ◽  
Ho-Chul Park ◽  
Qing Cai ◽  
Mdhasibur Rahman ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Travel Time ◽  
Support System ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Management Strategies ◽  
Travel Time Reliability ◽  
Traffic Condition ◽  
Traffic Conditions

A new decision support system (DSS) using travel time reliability was developed for integrated active traffic management (IATM) including freeways and arterials. The DSS consists of recommendation and evaluation of response plans. The DSS also includes three representative traffic management strategies: variable speed limits, queue warning, and ramp metering. The recommendation of response plans for recurring traffic congestion was generated from the logics of the three strategies. The evaluation of response plans was conducted by travel time reliability through the prediction of traffic conditions with response plans. The near-future prediction of traffic conditions with control strategies was conducted through METANET for freeways and arterials. The developed DSS was evaluated under three types of traffic congestion: extreme, heavy, and moderate. According to the evaluation results, the developed DSS recommended an IATM strategy with the highest synergistic relationships in real time and contributed to enhancing the effectiveness of the IATM strategies. It was confirmed that arterials should have the allowable residual capacity for the improvement of traffic flow of the entire corridor network. Furthermore, the DSS demonstrated a more balanced traffic condition between freeways and arterials.

scholarly journals Development and Usage of Travel Time Reliability Model for Urban Road Network under Ice and Snowfall Conditions

1970 ◽  
Vol 24 (5) ◽  
pp. 395-403 ◽  
Author(s):  
Jun-Qiang Leng ◽  
Yu-Qin Feng ◽  
Ya-Ping Zhang ◽  
Yi He
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Reliability ◽  
Weighted Average ◽  
Evaluation Methodology ◽  
Correction Function ◽  
Reliability Model ◽  
Travel Time Reliability ◽  
The Road ◽  
Set Up

This paper discusses the travel time reliability of road network under ice and snowfall conditions. With the introduction of correction function for the influence of ice and snowfall conditions on free travel time and capacity, the function of travel time was established. According to the limitation of the current travel time reliability, the new definition was defined on the basis of quantifying the relationship between LOS (Level of Service) and travel time reliability. The breakthrough of the traditional idea that the route travel time reliability model was set by general series system was made by considering the route as a whole unit; instead of using a paralleling system; another breakthrough was made to calculate the weighted average travel time reliability of OD (Original Destination) pair. On the basis of OD pair travel time reliability, the road network reliability model was set up. A partial road network was taken as an example to validate the effectiveness and practicality of the evaluation methodology.

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