Origin–Destination-Based Travel Time Reliability

2017 ◽  
Vol 2643 (1) ◽  
pp. 139-159 ◽  
Author(s):  
Shu Yang ◽  
Chengchuan An ◽  
Yao-Jan Wu ◽  
Jingxin Xia

Travel time reliability (TTR) is an important performance indicator for transportation systems. TTR can be generally categorized as either segment based or origin–destination (O-D) based. A primary difference between the two TTR estimations is that route information is implied in segment-based TTR estimations. Segment-based TTR estimations have been widely studied in previous research; however, O-D–based TTR estimations are used infrequently. This paper provides detailed insight into O-D–based TTR estimations and raises three new issues: ( a) How many routes do travelers usually take and what are the TTR values associated with these routes? ( b) Do statistical differences exist between route-specific and non-route-specific (NRS) TTR values? ( c) How can O-D–based TTR information be delivered? Two processes were proposed to address the issues. Three TTR measures—standard deviation, coefficient of variation, and buffer index—were calculated. The bootstrapping technique was used to measure the accuracy of the TTR measures. Approximate confidence intervals were used to investigate statistically the differences between route-specific and NRS TTR measures. A large quantity of taxicab GPS-based data provided data support for estimating O-D–based TTR measures. The results of O-D–based TTR measures showed that no statistically significant differences existed between route-specific and NRS TTR measures for most of the time periods examined. Statistically significant differences could still be found in some time periods. Travelers may take advantage of these differences to choose a more reliable route. Access to both numeric TTR values and route preference, instead of just to TTR information on segments of interest, can be beneficial to travelers in planning an entire trip.

Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Praveena Penmetsa

State agencies, regional agencies, cities, towns, and local municipalities design and maintain transportation systems for the benefit of users by improving mobility, reducing travel time, and enhancing safety. Cost–benefit analysis based on travel time savings and the value of reliability helps these agencies in prioritizing transportation projects or when evaluating transportation alternatives. This paper illustrates the use of monetary values of travel time savings and travel time reliability, computed for the state of North Carolina, to help assess the impact of transportation projects or alternatives. The results obtained indicate that, based on the illustration of the effect and impact of various transportation projects or alternatives, both improved travel time and reliability on roads yield significant monetary benefits. However, from cost–benefit analysis, it is observed that greater benefits can be achieved through improved reliability compared with benefits from a decrease in travel time for a given section of road.


2011 ◽  
Vol 97-98 ◽  
pp. 952-955
Author(s):  
Xiong Fei Zhang ◽  
Rui Min Li ◽  
Min Liu ◽  
Qi Xin Shi

Travel time reliability, as a measure of performance, is attracting more and more attention because unreliable transportation information hinders travelers’ decision making and creates difficulties for authorities to manage network operations. Since travel time reliability is closely related to the stochastic properties of the day-to-day travel time distribution, several statistical measures have been proposed, including standard deviation, coefficient of variation, buffer index, misery index and so on. Each of these measures is derived from travel time distribution but captures only one or two characteristics of travel time. In this paper, an effort is made to evaluate travel time reliability incorporating as many characteristics of travel time as possible based on fuzzy logic. The basic rules are: (1) the larger the variance is, the more unreliable the travel time is; (2) the larger the travel times of unlucky travelers are, the more unreliable the travel time is; (3) the larger the distribution skews to the left, the more unreliable the travel time is. The proposed methodology has been tested and analyzed with field data.


2011 ◽  
Vol 186 ◽  
pp. 556-559
Author(s):  
Lian Xue ◽  
Dan Jie Zhao ◽  
Gui Mei Liu

The development of the city's public transport system has an indispensable role to alleviate the pressure of urban roads. Bus travel time reliability is an important evaluation index of the bus operation service level. The simulation of bus travel time helps us understand the reliability of bus running time. In this paper, we use Monte Carlo stochastic simulation method to calculate the reliability of bus travel time. On this basis, we establish a model of the reliability of public transportation systems to research the reliability of bus travel time.


Author(s):  
Whoibin Chung ◽  
Mohamed Abdel-Aty ◽  
Juneyoung Park ◽  
Raj Ponnaluri

Traffic data from private-sector sources is increasingly used to estimate the travel time reliability of major road infrastructure. However, there is as yet no study evaluating the difference in estimating travel time reliability between the private-sector data and automated vehicle identification (AVI) based on radio frequency identification. As ground truth data, the AVI data were collected from an AVI system using toll tags and aggregated into five-minute intervals. As one of the representative traffic information providers, data from HERE was obtained through the Regional Integrated Traffic Information System, calculated in five-minute intervals. For the comparison, four kinds of measures were selected and estimated on the basis of the day of the week, specific time periods, and time of day in five-minute, 15-minute, and one-hour intervals. The statistical difference in travel time reliability was assessed through paired t-tests. According to the results, AVI and HERE data are comparable based on day of the week, specific time periods, and time of day at one-hour intervals, whereas at five-minute and 15-minute intervals, HERE and AVI data are not generally comparable. Thus, when estimating travel time reliability in real time, travel time reliability derived from HERE data may be different from the true travel time reliability. Considering that private-sector traffic data are currently used to estimate travel time reliability measures, the measures should be harmonized on the basis of robust statistics to provide more consistent measures related to the true travel time reliability.


Author(s):  
Sina Arefizadeh ◽  
Alireza Talebpour

Platooning is expected to enhance the efficiency of operating automated vehicles. The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogenous roadway segments. However, the transportation system consists of inhomogeneous segments, and understanding the full impacts of platooning requires investigation in a realistic setup. One of the main reasons for inhomogeneity is speed limit fluctuations. Speed limit changes frequently throughout the transportation network, due to safety-related considerations (e.g., changes in geometry and workzone operations) or congestion management schemes (e.g., speed harmonization systems). In the current transportation systems with human-driven vehicles, these speed drops can potentially result in shockwave formation, which can cause travel time unreliability. Automated vehicles, however, have the potential to prevent shockwave formation and propagation and, therefore, enhance travel time reliability. Accordingly, this study presents a constant time headway strategy for automated vehicle platooning to ensure accurate tracking of any velocity profile in the presence of speed limit fluctuations. The performance of the presented platooning strategy is compared with Gipps’ car-following model and intelligent driver model, as representatives of regular non-automated vehicles. Simulation results show that implementing a fully autonomous system prevents shockwave formation and propagation, and enhances travel time reliability by accurately tracking the desired velocity profile. Moreover, the performance of platoons of regular and automated vehicles is investigated in the presence of a speed drop. The results show that as the market penetration rate of automated vehicles increases, the platoon can track the velocity profile more accurately.


2020 ◽  
Vol 11 (2) ◽  
pp. 44-55
Author(s):  
Prosper S. Nyaki ◽  
Hannibal Bwire ◽  
Nurdin K. Mushule

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.


Author(s):  
Chao Chen ◽  
Alexander Skabardonis ◽  
Pravin Varaiya

Statistics from a corridor along Interstate 5 in Los Angeles show that average travel time and travel-time variability are meaningful measures of freeway performance. Variability of travel time is an important measure of service quality for travelers. Travel time can be used to quantify the effect of incidents, and incident information can help reduce travel-time uncertainty. Predictability of travel time is a measure of the benefits of intelligent transportation systems. These measures differ from those defined in the Highway Capacity Manual and other aggregate measures of delay.


Author(s):  
Sharmili Banik ◽  
Anil Kumar ◽  
Lelitha Vanajakshi

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