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.

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
Keyword(s):  
Open Source ◽  
Travel Time ◽  
Ad Hoc ◽  
Rainfall Intensity ◽  
Weather Data ◽  
Travel Time Reliability ◽  
Time Data ◽  
Rainy Weather ◽  
Average Travel Time ◽  
The Impact

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.

Evaluating the Impact of Highway-Railway Grade Crossings on Travel Time Reliability on a Highway Network Level

2018 ◽  
Vol 2672 (10) ◽  
pp. 1-11 ◽  
Author(s):  
Zifeng Wu ◽  
Laurence R. Rilett ◽  
Yifeng Chen
Keyword(s):  
Travel Time ◽  
Traffic Information ◽  
Area Network ◽  
Travel Time Reliability ◽  
Highway Traffic ◽  
Time Data ◽  
Highway Network ◽  
Grade Crossings ◽  
The Impact ◽  
Series Systems

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.

scholarly journals A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chenming Jiang ◽  
Linjun Lu ◽  
Junliang He ◽  
Caimao Tan
Keyword(s):  
Travel Time ◽  
Equilibrium Model ◽  
Route Choice ◽  
Weather Condition ◽  
Weather Conditions ◽  
User Equilibrium ◽  
Network Equilibrium ◽  
Adverse Weather Conditions ◽  
Adverse Weather Condition ◽  
Adverse Weather

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

Risk Assessment of Cascading Failure Considering Adverse Weather Condition and UPFC Corrective Control

2014 ◽  
Vol 631-632 ◽  
pp. 718-722
Author(s):  
Peng Jia Shi ◽  
Neng Xu ◽  
Jian Ying Chen ◽  
Chuang Xin Guo
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Probability Model ◽  
Weather Condition ◽  
Cascading Failure ◽  
Unified Power Flow Controller ◽  
Security Assessment ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

This paper investigates the effect of Unified Power Flow Controller (UPFC) on the risk of cascading failure which resulted from adverse weather based on the risk-based security assessment (RBSA) method. An improved piecewise probability model is described to reflect the impact of adverse weather condition and overloads. Based on power-injection model of UPFC, an optimal power flow control strategy is integrated into the RBSA procedure to relieve the risk of cascading failure caused by initiating event. Furthermore, a sensitivity based approach is adopted to determine the optimal location of UPFC. The effectiveness of the proposed method has been tested on IEEE RTS-79 system.

Validation and Calibration of Freeway Reliability Methodology in the Highway Capacity Manual: Method and Case Studies

2018 ◽  
Vol 2672 (15) ◽  
pp. 93-104 ◽  
Author(s):  
Nabaruna Karmakar ◽  
Seyedbehzad Aghdashi ◽  
Nagui M. Rouphail ◽  
Billy M. Williams
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Practical Implementation ◽  
Travel Time Reliability ◽  
Time Data ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Reliability Performance ◽  
The Impact

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.

scholarly journals Quantifying the Impact of Weather Events on Travel Time and Reliability

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xu Zhang ◽  
Mei Chen
Keyword(s):  
Quantile Regression ◽  
Travel Time ◽  
Time Window ◽  
Transportation System ◽  
Practical Significance ◽  
Travel Time Reliability ◽  
Average Delay ◽  
Weather Events ◽  
Adverse Weather ◽  
The Impact

It is of practical significance to understand the specific impact of weather events on the operating condition of the surface transportation system so that proactive and reactive strategies can be quickly implemented by transportation agencies to minimize the negativity resulted from adverse weather events. Many studies have been conducted on quantifying such effects yet suffer from limitations such as subjectively defining a time window under uncongested conditions and not being able to account for the severe impact from weather events which result in travel time unreliability. To overcome those shortcomings in existing literature, an integrated data mining framework based on decision tree and quantile regression techniques is developed in this study. The results demonstrate that the approach is effective in characterizing time periods with different traffic characteristics and quantifying the impact of rain and snow events on both congestion and reliability aspects of the transportation system. It is observed that snow events impose more significant impact on travel times than that from rain events. In addition, the impact from weather events is even more severe on travel time reliability than average delay. The impact magnitude is directly related to the level of recurrent congestion under study. Other insights with regard to the capability of quantile regression and future improvement on the methodological design are also offered.

Travel Time Variability and Congestion Assessment for Origin–Destination Clusters through the Experience of Mobility Companies

2020 ◽  
Vol 2674 (12) ◽  
pp. 103-117
Author(s):  
Haleh Ale-Ahmad ◽  
Ying Chen ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Network Performance ◽  
Transportation Network ◽  
Time Of Day ◽  
Time Variability ◽  
Travel Times ◽  
Time Data ◽  
Unit Distance ◽  
Reliability Performance ◽  
Network Approaches

Reliability is a measure of network performance that reflects the ability of the network to provide predictable travel times. Deviations from planned travel times can increase travel costs for users. To improve the system's performance, it is crucial to identify sources of unreliability, particularly the location on the network of unreliable performance. The large amount of travel time data recorded by Transportation Network Providers (TNPs) in recent years has enabled researchers to study the performance of entire networks. In this study, a real-world dataset provided by TNPs in Chicago is used to determine time of day, and day of week distribution of travel time per unit distance for origin–destination (OD) pairs. Eight measures of reliability are calculated for OD pairs in the network. Standard deviation (SD), planning time index (PTI), and on-time measure (PR) are used for a network-wide comparison of reliability performance. K-means clustering is performed on more than 21.3 million trips to divide 3,450 eligible OD pairs in the Chicago network into three groups with low, medium, and high intensity of each reliability metric. Lastly, metrics in each cluster of SD, PTI, and PR are compared. The results show that ranking PTI and PR is not sufficient for identifying unreliable/congested OD pairs in the network. Approaches for comparing reliability performance over different periods of the day for the same segment and over different segments in the network are discussed, along with network-wide measures of reliability.

scholarly journals Analyzing travel time belief reliability in road network under uncertain random environment

2021 ◽  
Author(s):  
Yi Yang ◽  
Siyu Huang ◽  
Meilin Wen ◽  
Xiao Chen ◽  
Qingyuan Zhang ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Environment ◽  
Road Network ◽  
Path Selection ◽  
Travel Time Reliability ◽  
Time Data ◽  
Urban Road Network ◽  
Urban Road ◽  
Starting Point ◽  
The Impact

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.

Modeling Arrival Flight Times within the Terminal Airspace

2021 ◽  
pp. 036119812110114
Author(s):  
Osama Alsalous ◽  
Susan Hotle
Keyword(s):  
United States ◽  
Travel Time ◽  
The United States ◽  
Time Of Day ◽  
Civil Aviation ◽  
Weather Data ◽  
Travel Times ◽  
International Airport ◽  
Area Of Interest ◽  
The Impact

Air traffic management efficiency in the descent phase of flights is a key area of interest in aviation research for the United States, Europe, and recently other parts of the world. The efficiency of arrival travel times within the terminal airspace is one of nineteen key performance indicators defined by the Federal Aviation Administration (FAA) and the International Civil Aviation Organization, typically within 100 nmi of arrival airports. This study models the relationship between travel time within the terminal airspace and contributing factors using a multivariate log-linear model to quantify the impact that these factors have on the total travel time within the last 100 nmi. The results were compared with the baseline set of variables that are currently used for benchmarking at the FAA. The analyzed data included flight and weather data from January 1, 2018 to March 31, 2018 for five airports in the United States: Chicago O’Hare International Airport, Hartsfield-Jackson Atlanta International, San Francisco International Airport, John F. Kennedy International Airport, and LaGuardia Airport. The modeling results showed that there is a significant improvement in prediction accuracy of travel times compared with the baseline methodology when additional factors, such as wind, meteorological conditions, demand and capacity, ground delay programs, market distance, time of day, and day of week, are included. Root mean squared error values from out-of-sample testing were used to measure the accuracy of the estimated models.

Robust Route Guidance Model Based on Advanced Traveler Information Systems

2005 ◽  
Vol 1935 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jiangang Lu ◽  
Xuegang Ban ◽  
Zhijun Qiu ◽  
Fan Yang ◽  
Bin Ran
Keyword(s):  
Travel Time ◽  
Random Variable ◽  
Time Variability ◽  
Route Guidance ◽  
Time Data ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Long Tails ◽  
The Right ◽  
The Impact

In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.

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