Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design

Abstract

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.

Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck

“…Traffic congestion in Karachi, Pakistan, is estimated to cost fifty thousand dollars daily [1]. This congestion can be mitigated through sustainable system design, such as realtime optimization of traffic signal patterns [2]. Traffic flow is typically analyzed using speed and density [3].…”

Lessons Learnt from Bridge Collapse: A View of Sustainable Management

“…Urbanization leads to a continuous increase in demand for urban infrastructures, including bridges, highways, water supply and sewage systems, electric and electronic pipelines and metro systems [1][2][3][4][5][6][7][8]. However, the service life of these infrastructural facilities often falls short of the designed life due to natural phenomena (e.g., earthquakes, floods, typhoons, ground subsidence) and lack of proper expertise and sustainable concept of the management layer [9][10][11][12][13][14][15][16]. Some facilities collapsed during the construction period, while others failed during their service life [17][18][19][20][21][22][23][24][25], which had harmful impacts on the social community and environment [26,27].…”

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

“…Accurately predicting traffic flow and grasping the dynamic change trend of traffic flow are key steps of ITSs, which are of great significance for alleviating traffic congestion, developing reliable traffic control and guidance strategies, and studying vehicle-road collaboration and autonomous driving [3][4][5][6]. Experience has shown that the monitoring and management of traffic states predicting in advance are often less effective than on-site deployment measures, and the benefits obtained by on-site deployment measures are more obvious [7,8]; therefore, traffic flow forecasting with big data technology has become the hottest research topic in the domain of traffic prediction [9,10].…”