Quantitative Evaluation of Advanced Traffic Management Systems using Analytic Hierarchy Process

With technological advancements in recent years, a series of intelligent transportation system (ITS) products have now become available to the transportation agencies to collect data and manage traffic conditions on the roadway network. Among ITS products, the advanced traffic management system (ATMS) has been effectively serving as the central nervous system of a traffic management center. ATMS serves as an integrated application for a wide variety of purposes ranging from data collection to implementing traffic management strategies. Owing to commercial popularity, a series of ATMS products are now available to transportation agencies and there is no consensus on selecting the best-suited product based on tailored requirements. Making a decision for a decision-critical item such as ATMS products on qualitative evidence can add risk to the decision-makers to justify their decision of choice. In this study, a multi-criteria decision analysis framework was proposed for quantitative evaluation of ATMS alternatives that can consider multiple and conflicting decision-making criteria using a real-world example. Moreover, the proposed framework was evaluated for different scenarios related to different applications of ATMS products to provide flexibility to the user in evaluating the ATMS alternatives. Results indicated that the proposed method can be considered as a viable alternative in contrast to a qualitative evidence-based decision-making strategy to minimize the risk associated with the decision-makers. Using the proposed quantitative framework, decision-makers can examine the weights of different criteria under consideration and evaluate multiple ATMS alternatives based on their jurisdiction-specific requirements. The proposed framework can be easily applied to other ITS technology selection processes.

Crash Classification by Congestion Type for Highways

Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management

Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency.

Evaluating the Emission of CO2 at Traffic Intersections with the Purpose of Reducing Emission Rate, Case Study: The University of Nigeria, Nsukka

Traffic congestion is a major problem around the world that results in slower speeds, increased trip time, and a longer queuing of vehicles. The production and use of fuels for vehicles results in emissions of greenhouse gases (GHSs), besides carbon dioxide, which include methane and nitrous oxide. Traffic lights that wirelessly keep track of vehicles could reduce journey time and fuel consumption thereby reducing carbon emissions. In view of the importance of vehicles as an emitter of GHGs, namely CO2, with the growing concern about climate change, this paper aims to explore the emission of CO2 from vehicles at a traffic intersection for the purpose of reducing emission rate. Realizing this reduction, points to the implementation of an Advanced Traffic Management System (ATMS) with Wireless Sensor Networks (WSNs) on the road network of a region will be discussed. With such a technology, a region can experience lower queue lengths at an intersection and therefore lower CO2 emission surrounding the area. The University of Nigeria, Nsukka (UNN) is used as a case study in exploring this phenomenon which over the years has seen a drastic increase on the amount of cars on the campus area. With the assumption that an ATM system with WSNs is deployed on the UNN campus area, the paper looks into the traffic dynamics that makes it possible to evaluate CO2 emission at traffic light intersections to ensure a cleaner environment. Throughout the paper, it will be made clear that with the relevant equation of CO2 emission and the arrival time per vehicle, CO2 emission rate can be evaluated at a traffic intersection depending on the volume of cars at the intersection. With such evaluation, further analysis can be made on ways to actually reduce CO2 emission and techniques for implementation with an ATM system.

Elaborating the Human Aspect of the NIST Framework for Cyber-Physical Systems

The National Institute of Standards and Technology (NIST) has developed a Framework for Cyber-Physical Systems (CPS Framework) that supports system engineering analysis, design, development, operation, validation and assurance of CPS. Cyber-physical systems (CPS) comprise interacting digital, analog, physical, and human components engineered for function through integrated physics and logic. For instance, a city implementing an advanced traffic management system including real-time predictive analytics and adaptation/optimization must consider all aspects of such a CPS system of systems’ functioning and integrations with other systems, including interactions with humans. One Aspect (or grouping of stakeholder concerns) of the CPS Framework is the Human Aspect. NIST is engaging HFES in a panel discussion to elaborate Human Aspect concerns, such as constructs, measures, methods, and tools.

Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze

Traffic managers strive to have the most accurate information on road conditions, normally by using sensors and cameras, to act effectively in response to incidents. The prevalence of crowdsourced traffic information that has become available to traffic managers brings hope and yet raises important questions about the proper strategy for allocating resources to monitoring methods. Although many researchers have indicated the potential value in crowdsourced data, it is crucial to quantitatively explore its validity and coverage as a new source of data. This research studied crowdsourced data from a smartphone navigation application called Waze to identify the characteristics of this social sensor and provide a comparison with some of the common sources of data in traffic management. Moreover, this work quantifies the potential additional coverage that Waze can provide to existing sources of the advanced traffic management system (ATMS). One year of Waze data was compared with the recorded incidents in the Iowa’s ATMS in the same timeframe. Overall, the findings indicated that the crowdsourced data stream from Waze is an invaluable source of information for traffic monitoring with broad coverage (covering 43.2% of ATMS crash and congestion reports), timely reporting (on average 9.8 minutes earlier than a probe-based alternative), and reasonable geographic accuracy. Waze reports currently make significant contributions to incident detection and were found to have potential for further complementing the ATMS coverage of traffic conditions. In addition to these findings, the crowdsourced data evaluation procedure in this work provides researchers with a flexible framework for data evaluation.