Evaluation of diversion strategies using dynamic traffic assignment

Abstract

A framework for the evaluation of the effectiveness of traffic diversion strategies for non-recurrent congestion, based on predictive guidance and using dynamic traffic assignment, is presented. Predictive guidance is based on a short-term prediction of traffic conditions, incorporating user reaction to information and guidance. A case study of the Lower Westchester County network in New York State, using DynaMIT-P, is presented to illustrate the application of the framework. DynaMIT-P is capable of evaluating diversion strategies based on predicted conditions, which take into account drivers' response to traffic information. The case study simulates the operations of predictive variable message signs positioned in strategic locations. DynaMIT-P is calibrated for the study network and used to establish base conditions for two incident scenarios in the absence of advanced traveller information systems. The effectiveness of predictive diversion strategies is evaluated (using rigorous statistical tests) by comparing traffic conditions with and without diversion strategies. The empirical findings suggest that incident diversion strategies based on predictive guidance result in travel time savings and increased travel time reliability.

Analyzing drivers’ preferences and choices for the content and format of variable message signs (VMS)

“…With respect to technological solutions, various intelligent transport systems have been implemented in managing demand to match the capacity available. These include: intelligent traffic signals (Balaji et al, 2010), ramp metering (Papageorgiou et al, 2004) and advanced traveler information system (ATIS) (Antoniou et al, 2011) through variable message signs (VMS). VMS, which is an infrastructure-based ATIS, has been proven to be an effective measure for alleviating traffic congestion caused by roadworks, special events, incidents and accidents affecting drivers' route diversion decisions (Mammar et al, 1996;Yim and Ygnace, 1996;Adler and Blue, 1998;Peeta and Ramos, 2006).…”

Determinants of driver response to variable message sign information in Athens

“…The traditional direction of new road construction, which has been proven to be a short-term solution, has been replaced by strategies that aim at the reduction of passenger car use and/or management. This can be achieved through public transport promotion (for an example see [2]), car-pooling and vehicle-sharing promotion [3,4], introduction of high occupancy lanes or additional economic incentives [5], road charging [6], as well as strategies that aim at managing the network traffic at an optimal manner through the implementation of various intelligent transport systems, such as intelligent traffic signals [7], ramp metering [8] and advanced traveller information systems [9][10][11]. Variable message signs (VMSs) belong to the latter category and form part of the transport network infrastructure in several bigger and smaller cities or roads, and are usually supported by local traffic management centres.…”

An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models

“…It consists of (i) a microscopic demand simulator that disaggregates OD flows into individual travelers and simulates their demand choices, including departure time, route choice, etc., (ii) a mesoscopic supply simulator that simulates the movements of vehicles in the network based on segment-specific speed-density relationships and capacities, and (iii) a learning model that captures the complex interactions between the demand side and the supply side. DynaMIT has been successfully applied in a variety of cities, including Los Angeles, California (Wen et al, 2006), Lower Westchester County, New York (Rathi et al, 2008;Antoniou et al, 2011a), and Boston, Massachusetts . DynaMIT's ability to model highly congested urban networks was shown in a recent case study in the city of Beijing (Ben-Akiva et al, 2012).…”

“…c o m / l o c a t e / t r c 2004; Tampère et al, 2010), and real-time traffic systems to provide consistent traffic prediction to travelers for route planning (e.g. Paz and Peeta, 2009) and to traffic control centers for control strategy generation (see, e.g., Ben-Akiva et al, 1997;Mahmassani, 2001;Antoniou, 2004;Wen et al, 2006, andAntoniou et al, 2011a). The implementation of simulation-based DTA systems in large-scale highly congested urban networks has been made possible by research in scalable DTA algorithms and parallel DTA simulation (Wen, 2009).…”