Data analytics approach for travel time reliability pattern analysis and prediction

Chen, Zhen; Fan, Wei

doi:10.1007/s40534-019-00195-6

Cited by 24 publications

(15 citation statements)

References 24 publications

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“…is research assumed that the traffic congestion occurs when the travel time is higher than twice the free-flow travel time. e free-flow speed of each route is obtained as the 85 th percentile speed during overnight hours (10 p.m. to 5 a.m.) [9,31,32]. Note that the free-flow travel time of each route is inversely proportional to free-flow speed; thus, the free-flow travel time is defined as the 15 th percentile travel time during overnight hours.…”

Section: Travel Time Reliability Measuresmentioning

confidence: 99%

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

Zou

Zhu

Xie

et al. 2020

Journal of Advanced Transportation

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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.

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Section: Travel Time Reliability Measuresmentioning

confidence: 99%

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

Zou

Zhu

Xie

et al. 2020

Journal of Advanced Transportation

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“…ey recommended using 95th percentile travel and buffer time as reliability indicators for bus routes [7]. Some recent route-based studies can be found in [8][9][10][11][12]. Because the focus of route-based studies is usually on one or a few specific routes, data were usually project-specific and most were discarded upon completion of a project, which makes continuous investigation difficult and sometimes impossible.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Chen

Liu

et al. 2020

Journal of Advanced Transportation

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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.

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“…They also investigated the impact of incidents on travel times. Chen and Fan ( 9 ) provided a systematic approach to the analysis of TTR patterns from probe vehicles and from conducting a case study on a freeway they found clear variations in the days-of-week (DOW) and weather conditions. Yazici et al ( 5 ) analyzed differences in travel time and variability patterns between urban roads and highways based on taxi GPS and automatic vehicle location data.…”

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confidence: 99%

“…Using these data, the travel time index (TTI) is studied as an example of a TTR measure. TTI is preferred over other TTR measures such as the planning time index (PTI) used by Chen and Fan ( 9 ), because the average travel time used by the TTI is meaningful even for a lower number of observations in a sparse data setting and because it is easy to interpret. The average travel time’s disadvantage of changing over time ( 9 ) has not much influence in the setting of this study as it does not compare data of multiple years.…”

mentioning

confidence: 99%

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

Steinmaßl¹,

Kranzinger²,

Rehrl³

2021

Transportation Research Record: Journal of the Transportation R

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Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

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Data analytics approach for travel time reliability pattern analysis and prediction

Cited by 24 publications

References 24 publications

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

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

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

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

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