Abstract
Abstract: Estimating traffic volume at a link level is important to transportation planners, traffic engineers, and policy makers. More specifically, this vital parameter has been used in transportation planning, traffic operations, highway geometric design, pavement design, and resource allocation. However, traditional factor approach, regression-‐based models, and artificial neural network models failed to present network-‐wide traffic volume estimates because they rely on traffic counts for model development, and they all have inherent weaknesses. A review to previous research work and the state-‐of-‐practice clearly indicates that the Traditional Four-step Travel Demand Model (TFTDM) was generally based on large traffic analysis zones (TAZs) and networks consisting of high functional-class roads only. Consequently, this conventional modeling framework yielded a limited number of link traffic assignments with fairly high estimation errors. In the light of these facts and the obvious need of accurate network-wide traffic estimates, this review is conducted. In particular, this paper provides an extensive review of using traditional travel demand models for improved network-‐wide traffic volume estimation. The paper then focuses on the challenges and opportunities in achieving high-fidelity travel demand model (HFTDM). This review has revealed that, opportunities in relation to both technological advances and intelligent data present a substantial potential in developing the proposed HFTDM for a much more accurate traffic estimation at a network-‐wide level. Finally, the paper concludes with key findings from the review and provides a few recommendations for future research related to the topic.
Highlights
AND PURPOSETraffic volume estimates are important to transportation planners, traffic engineers, and policy makers
Literature review conducted in this research paper indicated that the traditional, sequential “four-step” modeling process is still holding firm as it is being used by the majority of transportation communities since it has been introduced in the1950’s to evaluate capital-intensive transportation infrastructure projects
The shortcomings of conventional model originated from such macroscopic scope, which has been recognized as insufficient to consider policy alternatives and incapable of providing accurate network-wide traffic volume estimates
Summary
Traffic volume estimates are important to transportation planners, traffic engineers, and policy makers They are used by many Departments of Transportation (DOTs) and highway agencies to help plan, build, and maintain transportation infrastructure at county, provincial, and national levels. Traffic volume estimates are most often obtained from count stations (either temporary or permanent) installed on limited locations covering mainly the upper functional class of the roadway network (arterial and up) due to expensive costs of installation and maintenance. Since traffic counts are not Traditional Four-step Travel Demand Model (TFTDM), developed in U.S more than fifty years ago, is still used by various highway and transportation agencies worldwide to predict traffic volumes for specific roadway network links at both local and regional levels despite well documented weaknesses and flaws [1,2]. Traditional models use skimmed roadway networks, which mostly ignore low-class roads (collectors and local streets)
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