2019

Reza Omrani, CIMA Canada Inc.
Amer Shalaby, University of Toronto
Goran Nikolic, Head, Central Region Traffic Office Ministry of Transportation Ontario
Ali Hadayeghi, CIMA+

The reliability and applicability of traffic operation analyses depends on their ability to integrate relevant input from disparate databases in a seamless and automated manner. Inputs include information on road geometry, traffic composition, and spatial referencing. These databases are collected by different agencies for different purposes. As a result, a common definition of roadway segments is lacking across various applications. This paper developed a systematic segmentation methodology that considers the needs of various operational and planning studies. A multi-level dynamic segmentation approach has been developed to address different levels of requirements for various studies: at the micro level, referring to the smallest roadway segmentation for traffic simulation studies; at the meso level, representing a combination of several micro segments for traffic operation studies; and, at the macro level, corresponding to planning studies. In this paper, the proposed methodology for the segmentation of freeway and arterial corridors in Ontario (Canada) is demonstrated. At each level, several criteria were selected to identify the locations where the roadway network needs stringent analysis. Next, a pilot study was designed to evaluate the proposed methodology. It was found that the new segmentation methodology can identify successfully areas of congestion and queue growth / dissipation. Finally, the proposed segmentation methodology was implemented for more than 6000 km of Ontario’s roadway network. The results of this study can assist researchers and road agencies with defining a systematic roadway segmentation that can be utilized for different types of projects, ranging from traffic operation to planning studies.

Zhongyu Wang, Shanghai Maritime University
Yufang Bai, Hangzhou Urban Comprehensive Transport Research Center
Rui Zhu, Urban and Rural Planning Institute, Shanghai Municipal Engineering Design Institute
Yanli Wang, Tongji University
Bing Wu, Tongji University
Yinhai Wang, University of Washington

How serious is the extra traffic impact induced by project construction on urban road networks and should the construction continue during planned special events is a critical problem. In this paper, a framework of methodologies and workflow is presented in analyzing the project construction induced traffic impact during special events. We first analyze the characteristics of the special events attracted and project construction induced traffic flow. We then show how to evaluate the project construction induced traffic impact during special events and propose some quantitative analysis methods, which are different in nature from traditional approach of traffic impact analysis (TIA). Some management and improvement strategies are subsequently proposed to mitigate the negative impact of project construction. A case study of a vertical construction during Expo 2010 in Shanghai is discussed as an application of the proposed framework. The results show that the traffic impact of this vertical construction during Expo is moderate. Through our subsequent monitoring this vertical construction practice during Expo 2010, it can be concluded that the analysis method is reliable, the negative traffic impact of vertical construction to Expo is insignificant, and the suggested operation and management strategies are effective.

Aidin Massahi, Florida International University
Mohammed Hadi, Florida International University
Kollol Shams, Traffic Engineer , HDR Engineering, Inc
Mohamadtaqi Baqersad, Florida International University

The implementation of incident responsive signal control strategies are effective in relieving congestions during incidents. The goal of this paper is to develop and evaluate a methodology to assess the impacts of incident responsive signal control. This study demonstrates the utilization of a multi-resolution and multi-scenario modeling approach to support the evaluation and design of incident management strategies and the associated incident responsive signal control. The approach utilizes the strength of mesoscopic simulation-based dynamic traffic assignment modeling to determine route diversion and microscopic simulation to estimate the traffic impacts of incident responsive signal timing. The methods also utilize detailed traffic and incident data to inform the analysis, modeling, and simulation. The utilized methods support the modeling of different traffic patterns, confirm the diversion estimated by modeling tools based on field detector data, provide estimation of capacity drop during arterial incidents with different locations from upstream and downstream signals, and address the need for accurate turning movement volumes resulting from the modeling. The results from the simulation indicated that the implementation of incident responsive signal control strategies of the investigation case study was able to provide an improvement of 18.5% and 24.5% for the total delay of the thru movement in the incident direction for 30-minute and 45-minute arterial incidents, respectively. The corresponding reductions in total delay of all movements in the segment were 7.5% and 9.5%, respectively. These values can be used to inform the return-on-investment as a part of planning and operation analysis of Active Transportation Management (ATM) strategies.

David Greenburg, The Citadel
Dimitra Michalaka, The Citadel

Interstate 526 which forms a nearly complete loop around Charleston metro area and has been identified by SCDOT as one of the most congested corridors in the state. The James B. Edwards Bridge over the Wando River is part of I-526/I-26 transportation network and on May 14, 2018 a broken support cable forced the closure of the westbound lanes which had a significant negative impact on traffic flow throughout the Charleston area. To alleviate significant traffic congestion in the town of Mt. Pleasant, SCDOT reversed an eastbound lane of the James B. Edwards Bridge on I-526 to establish two-way traffic. Using traffic flow data obtained from SCDOT’s Traffic Polling and Analysis System (TPAS), forecast models were developed and the predicted traffic flow counts compared to the actual traffic flow counts to identify changes in traffic flow count behavior throughout the network during May and June of 2018. The close proximity of the Wando Welch shipping terminal and the high truck volume into and out of the port is considered in assessing the altered traffic flow behavior throughout the network. Analysis shows that the contraflow lane implementation improved flow rates into and out of the Mt. Pleasant area, and on the I-526 westbound contraflow lane while flow throughout the remainder of the network decreased. Secondary roads experienced increased traffic volumes throughout the network that extended beyond the normal peak commuting hours. Significant improvement to commuting times and traffic flow was only achieved after the re-opening of the westbound lanes.

Matej Cebecauer, KTH Royal Institute of Technology
David Gundlegård, Linköping University
Erik Jenelius, KTH Royal Institute of Technology
Wilco Burghout

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps has been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction, and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis is made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.