Optimum Routing of Freight in Urban Environments under Normal Operations and Disruptions using a Co-simulation Optimization Control Approach

The complexity and dynamics of the road and rail networks that are also shared by passengers together with the unpredictability of the effect of incidents, disruptions and demand, in temporal and spacial coordinates makes the scheduling and optimum routing of freight a very challenging task despite recent advances in information technologies. Estimating travel times in an urban road network environment is a real challenge especially during incidents and other disruptive events. Current practices rely on historical data and limited available real time information in order to make routing decisions that minimize a certain cost objective which in the case of road network is usually travel time. Incidents, disruptions, changes in demand, planned and unpredictable events may change the historical patterns of traffic, rendering decisions ineffective leading to imbalances in capacity across time and space coordinates. The purpose of this project is to exploit the availability of powerful computational and software tools together with advances in optimization and feedback control for dynamical systems in order to come up with a methodology that can lead to more efficient decisions in freight scheduling and routing. The method relies on the use of real time simulation models, for predicting travel times, traffic flows, fuel consumption and pollution by going beyond to what can be achieved today based on historical and limited real time data. The simulation models are used in a feedback loop with an optimization model and a load-balancing controller. The simulation models receive historical and streaming data and are able to automatically reconfigure themselves to simulate the subsequent effects of incidents and disruptions. They can be used to estimate costs (travel time, fuel consumption, pollution etc.) along different links in the network by fast-forwarding. In addition they can be used to test different decision scenarios before reaching a final decision. The predicted states of the network can be used to generate cost estimates along possible routes, which in turn can be used by an optimizer to calculate the optimum route with respect to space and time. In many cases however routing decisions and expected future demand may easily disturb the states of the network and change the initially estimated costs leading to an unbalanced network load in space and/or time. A load balancing controller exercises the simulation model and tests different load distributions along the possible routes. The approach leads to an iterative feedback process with the objective of reducing the value of the cost index further till a stopping criterion is met in which case the final decision is applied to the real system. The objective of this project is focused on the effectiveness of the methodology as a tool for freight scheduling and routing in a complex urban environment. The project will investigate whether the use of simulation models operating in real time with optimization and automated control techniques can provide much better decisions than existing approaches that rely on past data and limited real time information. Issues such as speed of computations, scalability, convergence, ability for fast reconfiguration during incidents and disruptions are important research problems. While most of the work will concentrate on developing and analyzing the main components of the proposed co-simulation optimization control approach all simulations, testing and evaluations will be carried out by using a validated microscopic simulation model of part of the rail and road network in the Los Angeles and Long Beach area that includes the two major ports. The deliverables of the project will include the demonstration of the co-simulation optimization control approach and its benefits in upgrading current practices to a new level that takes full advantage of available technologies that include computations and automation of decisions in addition to information technologies. Our experience with traffic models indicates that even though duplicating the real world is an impossible task, simulation models provide much better information than static or equilibrium models and therefore their use in managing freight flows and traffic in general offer a strong potential for dramatic improvements if properly used in combination with optimization and control techniques. This project is aimed to demonstrate this potential as a main deliverable.


  • English


  • Status: Completed
  • Funding: $100,000
  • Contract Numbers:


  • Sponsor Organizations:

    Research and Innovative Technology Administration

    University Transportation Centers Program
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Giuliano, Genevieve

  • Performing Organizations:

    National Center for Metropolitan Transportation Research

    University of Southern California
    650 Childs Way, RGL 107
    Los Angeles, CA  United States  90089-0626
  • Principal Investigators:

    Ioannou, Petros

  • Start Date: 20150701
  • Expected Completion Date: 20160630
  • Actual Completion Date: 0
  • Source Data: RiP Project 39747

Subject/Index Terms

Filing Info

  • Accession Number: 01572360
  • Record Type: Research project
  • Source Agency: National Center for Metropolitan Transportation Research
  • Contract Numbers: DTRT13-G-UTC57
  • Files: UTC, RIP
  • Created Date: Aug 6 2015 1:00AM