Path Finding in Stochastic Time Varying Networks with Spatial and Temporal Correlations for Heterogeneous Travelers

2016 ◽  
Vol 2567 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Ali Zockaie ◽  
Hani S. Mahmassani ◽  
Yu Nie
Keyword(s):  
Time Varying ◽  
Path Finding ◽  
Temporal Correlations ◽  
Time Varying Networks ◽  
Stochastic Time

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

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.

Dynamical Processes in Time-Varying Networks

2019 ◽  
pp. 66-89
Author(s):  
Bruno Gonçalves ◽  
Nicola Perra
Keyword(s):  
Mean Field ◽  
Future Research ◽  
Time Varying ◽  
Dynamical Processes ◽  
Time Resolved ◽  
Rumor Spreading ◽  
Temporal Correlations ◽  
Recent Developments ◽  
Time Varying Networks ◽  
Future Research Directions

Networks in almost any domain are dynamical entities. New nodes join the system, others leave it, and links describing their interactions are constantly changing. However, due to the absence of time-resolved data and mathematical challenges, the large majority of research in network science neglects these features in favor of static or mean-field representations. While such approximations are useful and appropriate in some systems and processes, they fail in many others where the co-occurrence, duration, and order of contacts are crucial ingredients. This chapter presents a review of recent developments in the study of temporal networks and dynamical processes unfolding on their fabrics. It focuses in particular on activity-driven networks as an empirically motivated and analytically tractable class of models of the time-varying network. Within this framework the chapter studies the effects of temporal connectivity patterns in random walks, the epidemic model, and the rumor spreading model. The results highlight the striking impact that temporal correlations have on dynamical processes taking place over time-varying networks. The chapter ends by considering future research directions and challenges in this important area.

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