scholarly journals Identification of Affecting Factors on the Travel Time Reliability for Bus Transportation

2021 ◽  
Vol 2 (1) ◽  
pp. 19-30
Author(s):  
Ahmed Hassan Mohamed ◽  
Ibrhim A I Adwan ◽  
Abobaker G. F Ahmeda ◽  
Hamza Hrtemih ◽  
Haitham Al-MSari
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Policy Implications ◽  
Service Reliability ◽  
Travel Time Reliability ◽  
Affecting Factors ◽  
Traffic Condition ◽  
Factors Affecting ◽  
Transit Agencies ◽  
Bus Transportation

Public bus transit travel time is affected by many factors, including traffic signals and traffic condition. Transit agencies have implemented transit signal priority (TSP) strategies to reduce transit travel time and improve service reliability. However, due to the lack of empirical data, these factors' collective impact and bus travel time strategies have not been studied at the stop-to-stop segment level. This research focuses on the factors affecting travel time reliability, emphasising the variability between operators and the policy implications of such differences. One-way analysis of variance (ANOVA) statistical methods have been used to assess the quality implications of public bus transportation time reliability. This research seeks to investigate the factors affecting the travel time (TT) reliability of bus transport. Studies were conducted along three bus routes serving different areas. Factors strongly related to TT reliability include route length, number of signalised intersections, day of the week, bus stops, departure delays, bus lane, passenger boarding and alighting, weather condition, and fare structure. Based on the proposed model factors affecting TT reliability, it was found that TT is strongly affected by the number of bus stoppings and also the length of the route. The reliability of all three routes during the weekday is low because of delays in departure. The number of signalised intersections along the route affects reliability. Meanwhile, more passengers boarding and paying cash increased the travel time reliability of buses.

scholarly journals Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Time Of Day ◽  
Weather Data ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Time Data ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

Commuter Bicyclist Route Choice: Analysis Using a Stated Preference Survey

2003 ◽  
Vol 1828 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Monique A. Stinson ◽  
Chandra R. Bhat
Keyword(s):  
Travel Time ◽  
Stated Preference ◽  
Empirical Models ◽  
Policy Implications ◽  
Facility Planning ◽  
Quality Factors ◽  
Stated Preference Survey ◽  
Factors Affecting ◽  
Choice Analysis ◽  
Using Data

The importance of factors affecting commuter bicyclists’ route choices was evaluated. Both route-level (e.g., travel time) and link-level (e.g., pavement quality) factors are examined. Empirical models are estimated using data from a stated preference survey conducted via the Internet. The models indicate that, for commuter bicyclists, travel time is the most important factor in choosing a route. Presence of a bicycle facility (especially a bike lane or separate path), the level of automobile traffic, pavement or riding surface quality, and presence of a bicycle facility on a bridge are also very important determinants. Furthermore, there are policy implications of these results for bicycle facility planning.

Managing Unplanned Rail Disruptions: Policy Implications and Guidelines Towards an Effective Bus Bridging Strategy

2019 ◽  
Vol 2673 (4) ◽  
pp. 473-489 ◽  
Author(s):  
Alaa Itani ◽  
Aya Aboudina ◽  
Ehab Diab ◽  
Siva Srikukenthiran ◽  
Amer Shalaby
Keyword(s):  
Performance Metrics ◽  
Policy Implications ◽  
Factors Affecting ◽  
Demand Reduction ◽  
Incident Duration ◽  
Transit Agencies ◽  
Bridging Strategy ◽  
Forecasting Errors ◽  
The City ◽  
Rail Service

Bus bridging is a key strategy used by transit agencies to handle rail service interruptions. In practice, buses are dispatched from scheduled services to act as temporary shuttles along the disrupted rail segment. This study provides a robust analysis of four factors affecting bus bridging policies: 1) initial dispatch direction of shuttle buses, 2) dispatch time (i.e., the response time for requesting shuttle buses), 3) uncertainty in predicting the incident duration, and 4) reduction of metro passengers demand because of disruption. A user delay modeling tool is used to assess various bus bridging policies based on their resulting users’ delays (for affected passengers) and other system performance measures. The tool was validated, and sensitivity analysis was conducted based on real disruption scenarios that suspended various segments of the metro service in the City of Toronto. The main results indicate that: 1) the initial dispatch direction of shuttle buses should take into consideration the demand at the disrupted segment while maintaining a moderate level of shuttle bus utilization; 2) a 1-min increase in the dispatch time causes about 0.4 min additional waiting time at disrupted metro stations per passenger; 3) incidents with high forecasting errors can cause excessive delays for metro passengers and significant wasted time of non-utilized shuttle buses; and, 4) significant users’ delay savings are observed at higher demand reduction levels. This paper provides transportation practitioners and planners with a better understanding of the different aspects of bus bridging policies based on users’ delays and shuttle buses’ performance metrics.

Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior

2012 ◽  
Vol 2275 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Yanshuo Sun ◽  
Ruihua Xu
Keyword(s):  
Travel Time ◽  
Choice Behavior ◽  
Route Choice ◽  
Service Reliability ◽  
Travel Time Reliability ◽  
Elapsed Time ◽  
Route Choice Behavior ◽  
Collection Data ◽  
Passenger Flow Assignment ◽  
Transfer Stations

Applications of automatic fare collection data were investigated, with a focus on analysis of travel time reliability and estimation of passenger route choice behavior. Beijing Metro was used as a case study. A rail journey was decomposed, and each component was studied with regard to the uncertainties involved. Methods were then designed and validated to infer platform elapsed time (PET) for through stations and platform elapsed time–transfer (PET-Trans) for transfer stations by using smart card transactional data, train schedules, and complementary manual surveys. With this information, the journey time distribution of any path can be established, and methods were proposed for inferring route choice proportions. After data preparation, the methods were applied to two typical origins and destinations from the Beijing Metro. Key values concerning travel time reliability, such as PET, PET-Trans, travelers left behind (unable to board), and path coefficients, were obtained and interpreted in detail. The outcome of this research could facilitate analysis of transit service reliability and passenger flow assignment in daily operations.

Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time

2005 ◽  
Vol 1917 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Mecit Cetin ◽  
George F. List ◽  
Yingjie Zhou
Keyword(s):  
Travel Time ◽  
Transportation Network ◽  
Travel Time Reliability ◽  
System State ◽  
Travel Times ◽  
State Estimate ◽  
Factors Affecting ◽  
Minimum Number ◽  
Time Information ◽  
Detection Technologies

Using probe vehicles rather than other detection technologies has great value, especially when travel time information is sought in a transportation network. Even though probes enable direct measurement of travel times across links, the quality or reliability of a system state estimate based on such measurements depends heavily on the number of probe observations across time and space. Clearly, it is important to know what level of travel time reliability can be achieved from a given number of probes. It is equally important to find ways (other than increasing the sample size of probes) of improving the reliability in the travel time estimate. This paper provides two new perspectives on those topics. First, the probe estimation problem is formulated in the context of estimating travel times. Second, a method is introduced to create a virtual network by inserting dummy nodes in the midpoints of links to enhance the ability to estimate travel times further in a way that is more consistent with the processing that vehicles receive. Numerical experiments are presented to illustrate the value of those ideas.

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 Travel Time Reliability of Bus Operation in Heterogeneous Traffic Conditions of Dar es Salaam City, Tanzania

2020 ◽  
Vol 11 (2) ◽  
pp. 44-55
Author(s):  
Prosper S. Nyaki ◽  
Hannibal Bwire ◽  
Nurdin K. Mushule
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Dar Es Salaam ◽  
Heterogeneous Traffic ◽  
Service Reliability ◽  
Travel Time Reliability ◽  
Activity Scheduling ◽  
Traffic Conditions ◽  
Deviation Coefficient

AbstractThe assessment of travel time reliability enables precise prediction of travel times, better activity scheduling and decisions for all users of the road network. Furthermore, it helps to monitor traffic flow as a crucial strategy for reducing traffic congestion and ensuring high-quality service in urban roads. Travel time reliability is a useful reference tool for evaluating transport service quality, operating costs and system efficiency. However, many analyses of travel time reliability do not provide true travel variation under heterogeneous traffic flow conditions where traffic flow is a mixture of motorized and non-motorized transport. This study analysed travel time reliability under heterogeneous traffic conditions. The travel reliabilities focused on passenger waiting time at bus stops, in-vehicle travel time, and delay time at intersections which were analysed using buffer time, standard deviation, coefficient of variation, and planning time. The data used were obtained from five main bus routes in Dar es Salaam. The results indicate low service reliability in the outbound directions compared to inbound directions. They also intend to raise awareness of policy-makers about the situation and to make them shift from expanding road networks towards optimising road operations.

scholarly journals Transit Signal Priority in Smart Cities

2020 ◽  
Author(s):  
Bahman Moghimi ◽  
Camille Kamga
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transit ◽  
Smart Cities ◽  
Light Rail ◽  
Service Reliability ◽  
Transit Signal Priority ◽  
Vicious Cycle ◽  
Urban Networks ◽  
Transit Agencies

Giving priority to public transport vehicles at traffic signals is one of the traffic management strategies deployed at emerging smart cities to increase the quality of service for public transit users. It is a key to breaking the vicious cycle of congestion that threatens to bring cities into gridlock. In that cycle, increasing private traffic makes public transport become slower, less reliable, and less attractive. This results in deteriorated transit speed and reliability and induces more people to leave public transit in favor of the private cars, which create more traffic congestion, generate emissions, and increase energy consumption. Prioritizing public transit would break the vicious cycle and make it a more attractive mode as traffic demand and urban networks grow. A traditional way of protecting public transit from congestion is to move it either underground or above ground, as in the form of a metro/subway or air rail or create a dedicated lane as in the form of bus lane or light rail transit (LRT). However, due to the enormous capital expense involved or the lack of right-of-way, these solutions are often limited to few travel corridors or where money is not an issue. An alternative to prioritizing space to transit is to prioritize transit through time in the form of Transit Signal Priority (TSP). Noteworthy, transit and specifically bus schedules are known to be unstable and can be thrown off their schedule with even small changes in traffic or dwell time. At the same time, transit service reliability is an important factor for passengers and transit agencies. Less variability in transit travel time will need less slack or layover time. Thus, transit schedulers are interested in reducing transit travel time and its variability. One way to reach this goal is through an active intervention like TSP. In this chapter a comprehensive review of transit signal priority models is presented. The studies are classified into different categories which are: signal priority and different control systems, passive versus active priority, predictive transit signal priority, priority with connected vehicles, multi-modal signal priority models, and other practical considerations.

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