Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information

• Published in: Computers & operations research Vol. 39; no. 2; pp. 358 - 373
• Main Authors: Güner, Ali R., Murat, Alper, Chinnam, Ratna Babu
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2012; Elsevier; Pergamon Press Inc
• Subjects: Algorithms; Applied sciences; Computer networks; Congestion; Delay; Dynamic programming; Dynamic routing; Dynamics; Exact sciences and technology; Ground, air and sea transportation, marine construction; Incidents; Inventory control, production control. Distribution; ITS; JIT logistics; Just in time; Logistics; Mathematical models; Mathematical programming; Networks; Operational research and scientific management; Operational research. Management science; Route optimization; Routing (telecommunications); Studies; Traffic congestion; Transportation; Transportation planning, management and economics; Dynamic routing; ITS; Incidents; JIT logistics; Congestion; Transportation; Performance evaluation; Dispatching problem; Recurrence; Transportation system; Transit time; Vehicle routing problem; Variability; Non stationary process; Modeling; Routing algorithm; Transportation network; Freeway; Delay time; Dynamic programming; Dynamic model; Intelligent system; Delayed time; Travel time; Non stationary condition; Routing; Real time; Stochastic programming; Production management; Just in time; Logistics
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2011.04.012

In just-in-time (JIT) manufacturing environments, on-time delivery is a key performance measure for dispatching and routing of freight vehicles. Growing travel time delays and variability, attributable to increasing congestion in transportation networks, are greatly impacting the efficiency of JIT logistics operations. Recurrent and non-recurrent congestion are the two primary reasons for delivery delay and variability. Over 50% of all travel time delays are attributable to non-recurrent congestion sources such as incidents. Despite its importance, state-of-the-art dynamic routing algorithms assume away the effect of these incidents on travel time. In this study, we propose a stochastic dynamic programming formulation for dynamic routing of vehicles in non-stationary stochastic networks subject to both recurrent and non-recurrent congestion. We also propose alternative models to estimate incident induced delays that can be integrated with dynamic routing algorithms. Proposed dynamic routing models exploit real-time traffic information regarding speeds and incidents from Intelligent Transportation System (ITS) sources to improve delivery performance. Results are very promising when the algorithms are tested in a simulated network of South-East Michigan freeways using historical data from the MITS Center and Traffic.com.