scholarly journals Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qi Zhang ◽  
Hong Chen ◽  
Hongchao Liu ◽  
Wei Li ◽  
Yibin Zhang

Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen

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.


Author(s):  
Zifeng Wu ◽  
Laurence R. Rilett ◽  
Yifeng Chen

Highway-rail grade crossings (HRGCs) have a range of safety and operational impacts on highway traffic networks. This paper illustrates a methodology for evaluating travel-time reliability for the routes and networks affected by trains traveling through HRGCs. A sub-area network including three HRGCs is used as the study network, and a simulation model calibrated to local traffic conditions and signal preemption strategies using field data is used as the platform to generate travel time data for analysis. Time-dependent reliability intervals for route travel time are generated based on route travel-time means and standard deviations. OD level reliability is calculated using a generic reliability engineering approach for parallel and series systems. The route travel time reliability results can be provided as real-time traffic information to assist drivers’ route-choice decisions. The OD level reliability is a way to quantify the impact of HRGCs on highway network operation. This effort fills the gap of reliability research for HRGCs on the route and sub-area network level, and contributes to improving the efficiency of decision-making for both traffic engineers and drivers.


Author(s):  
Nabaruna Karmakar ◽  
Seyedbehzad Aghdashi ◽  
Nagui M. Rouphail ◽  
Billy M. Williams

Traffic congestion costs drivers an average of $1,200 a year in wasted fuel and time, with most travelers becoming less tolerant of unexpected delays. Substantial efforts have been made to account for the impact of non-recurring sources of congestion on travel time reliability. The 6th edition of the Highway Capacity Manual (HCM) provides a structured guidance on a step-by-step analysis to estimate reliability performance measures on freeway facilities. However, practical implementation of these methods poses its own challenges. Performing these analyses requires assimilation of data scattered in different platforms, and this assimilation is complicated further by the fact that data and data platforms differ from state to state. This paper focuses on practical calibration and validation methods of the core and reliability analyses described in the HCM. The main objective is to provide HCM users with guidance on collecting data for freeway reliability analysis as well as validating the reliability performance measures predictions of the HCM methodology. A real-world case study on three routes on Interstate 40 in the Raleigh-Durham area in North Carolina is used to describe the steps required for conducting this analysis. The travel time index (TTI) distribution, reported by the HCM models, was found to match those from probe-based travel time data closely up to the 80th percentile values. However, because of a mismatch between the actual and HCM estimated incident allocation patterns both spatially and temporally, and the fact that traffic demands in the HCM methods are by default insensitive to the occurrence of major incidents, the HCM approach tended to generate larger travel time values in the upper regions of the travel time distribution.


2013 ◽  
Vol 361-363 ◽  
pp. 2255-2261 ◽  
Author(s):  
Li Juan Shi ◽  
Ting Jing ◽  
Xiao Hong Chen ◽  
Dong Xiu Ou

This paper presents an investigation of the effects of rainfall with different levels of precipitation intensity on urban expressway travel time under free-flow speed conditions. The traffic data and corresponding weather data from the expressway section of Longyang in Shanghai for more than one year were used. Statistical analysis was applied to investigate the effects of rainfall on travel time quantitatively. There are two major contributions of this paper. Firstly, four levels of rainfall have varying degrees of significant impacts on travel time in terms of average travel time. Slight rain has no influence on variability of travel time, while heavier rain increases the variability. Secondly, three travel time stochastic models: Normal, Lognormal and Weibull, were proposed. The Lognormal model is the best-fit model under good weather, slight and moderate rainfall conditions, while both Weibull model and Lognormal model are the preferable models under heavy rain and rainstorm conditions. The recommended Lognormal model can be further used for evaluating the performance of the Longyang expressway section in terms of travel time reliability under good weather, slight rain, moderate rain, heavy rain and rainstorm conditions respectively.


2021 ◽  
Author(s):  
Yi Yang ◽  
Siyu Huang ◽  
Meilin Wen ◽  
Xiao Chen ◽  
Qingyuan Zhang ◽  
...  

Abstract It is necessary to understand the operation status of the urban road network, especially when the network is complicated and uncertain. Taking travel time data as the starting point, we have studied the shortcomings of existing travel time reliability indicators. Most of them simplify or even ignore the information of traffic performance thresholds. According to the characteristics of the real urban road network, by extracting the information of the subject and object of the traffic service, we proposed measurement of the reliability of travel time in an uncertain random environment, that is, the travel time belief reliability, which takes the impact of the epistemic and random uncertainty on reliability into account. Next, we established the belief reliability model of travel task under the uncertain random road environment. The model considers path selection, departure status and road conditions, and gives a path selection algorithm under time-varying road network. Besides, using the uncertainty regression analysis method, we explored the impact of road objective factors and driving state factors on the travel time threshold. Finally, we took the actual travel task in Beijing as an example to verify the feasibility and practicability of the model and algorithm.


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.


Author(s):  
Tristan Cherry ◽  
Mark Fowler ◽  
Claire Goldhammer ◽  
Jeong Yun Kweun ◽  
Thomas Sherman ◽  
...  

The COVID-19 pandemic has fundamentally disrupted travel behavior and consumer preferences. To slow the spread of the virus, public health officials and state and local governments issued stay-at-home orders and, among other actions, closed nonessential businesses and educational facilities. The resulting recessionary effects have been particularly acute for U.S. toll roads, with an observed year-over-year decline in traffic and revenue of 50% to 90% in April and May 2020. These disruptions have also led to changes in the types of trip that travelers make and their frequency, their choice of travel mode, and their willingness to pay tolls for travel time savings and travel time reliability. This paper describes the results of travel behavior research conducted on behalf of the Virginia Department of Transportation before and during the COVID-19 pandemic in the National Capital Region of Washington, D.C., Maryland, and Northern Virginia. The research included a stated preference survey to estimate travelers’ willingness to pay for travel time savings and travel time reliability, to support forecasts of traffic and revenue for existing and proposed toll corridors. The survey collected data between December 2019 and June 2020. A comparison of the data collected before and during the pandemic shows widespread changes in travel behavior and a reduction in willingness to pay for travel time savings and travel time reliability across all traveler types, particularly for drivers making trips to or from work. These findings have significant implications for the return of travelers to toll corridors in the region and future forecasts of traffic and revenue.


Author(s):  
Malvika Dixit ◽  
Ties Brands ◽  
Niels van Oort ◽  
Oded Cats ◽  
Serge Hoogendoorn

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.


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