Optimal Routing of Hazardous Materials in Stochastic, Time-Varying Transportation Networks

1998 ◽  
Vol 1645 (1) ◽  
pp. 143-151 ◽  
Author(s):  
Elise Miller-Hooks ◽  
Hani S. Mahmassani
Keyword(s):  
Risk Measures ◽  
Hazardous Materials ◽  
Population Exposure ◽  
Time Varying ◽  
Key Factors ◽  
Highway Network ◽  
Time Varying Networks ◽  
Static Conditions ◽  
Stochastic Time ◽  
Selection Of

The selection of routes in a network along which to transport hazardous materials is explored, taking into consideration several key factors pertaining to the length of time of the transport and the risk of population exposure in the event of an incident. That travel time and risk measures are not constant over time and at best can be known with uncertainty is explicitly recognized in the routing decisions. Existing approaches typically assume static conditions, possibly resulting in inefficient route selection and unnecessary risk exposure. Several procedures for determining superior paths for the transport of hazardous materials in stochastic, time-varying networks are presented. These procedures and their extensions are illustrated systematically for an example application using the Texas highway network. The application illustrates the tradeoffs between the information obtained in the solution and computational efficiency, and highlights the benefits of incorporating these procedures in a decision-support system for hazardous material shipment routing decisions.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Online Charging and Routing of Electric Vehicles in Stochastic Time-Varying Networks

2017 ◽  
Vol 2667 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Ehsan Jafari ◽  
Stephen D. Boyles
Keyword(s):  
Travel Time ◽  
A Priori ◽  
Cost Savings ◽  
Time Varying ◽  
Charging Station ◽  
Current State ◽  
Time Varying Networks ◽  
Charging Stations ◽  
Generalized Cost ◽  
Stochastic Time

This paper formulates the problem of online charging and routing of a single electric vehicle in a network with stochastic and time-varying travel times. Public charging stations, with nonidentical electricity prices and charging rates, exist through the network. Upon arrival at each node, the traveler learns the travel time on all downstream arcs and the waiting time at the charging station, if one is available. The traveler aims to minimize the expected generalized cost—formulated as a weighted sum of travel time and charging cost—by considering the current state of the vehicle and availability of information in the future. The paper also discusses an offline algorithm by which all routing and charging decisions are made a priori. The numerical results demonstrate that cost savings of the online policy, compared with that for the offline algorithm, is more significant in larger networks and that the number of charging stations and vehicle efficiency rate have a significant impact on those savings.

Least possible time paths in stochastic,time-varying networks

1998 ◽  
Vol 25 (12) ◽  
pp. 1107-1125 ◽  
Author(s):  
Elise D. Miller-Hooks ◽  
Hani S. Mahmassani
Keyword(s):  
Time Varying ◽  
Time Varying Networks ◽  
Stochastic Time

scholarly journals Risk Assessment of Hazmat Road Transportation Considering Environmental Risk under Time-Varying Conditions

2021 ◽  
Vol 18 (18) ◽  
pp. 9780
Author(s):  
Liping Liu ◽  
Qing Wu ◽  
Shuxia Li ◽  
Ying Li ◽  
Tijun Fan
Keyword(s):  
Risk Assessment ◽  
Environmental Pollution ◽  
Environmental Risk ◽  
Hazardous Materials ◽  
Assessment Model ◽  
Population Exposure ◽  
Model Parameters ◽  
Time Varying ◽  
Road Transportation ◽  
Hazardous Materials Transportation

Hazardous materials shipments are integral to the development of industrial countries. Significant casualties and severe environmental pollution quickly ensue when accidents occur. Currently, relevant research on risk assessment of hazardous materials’ road transportation remains limited when both the population exposure risk and environmental risk are considered, especially in regard to analyzing the differences of accident impacts in different populations and environments. This paper adopts a Gaussian plume model to simulate dynamic areas at three levels of population exposure and assesses the pollution scope of air, groundwater, lakes, and rivers with a variety of diffusion models. Then, we utilize various costs to analyze the differences of accident impacts in population exposure and environmental pollution. Finally, a risk assessment model of hazardous materials road transportation under time-varying conditions is presented by considering the bearing capacity of the assessed area. Furthermore, this model is applied to a case study involving a risk assessment of hazardous materials transportation of a highly populated metropolitan area of Shanghai, China. The resulting analyses reveal that the safety of hazardous materials transportation could be effectively improved by controlling certain model parameters and avoiding road segments with a high risk of catastrophic accident consequences.

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