Abstract
The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements.
Highlights
The unprecedented growth of global urbanization has been accompanied by several critical issues such as air and noise pollution, insufficient public resource and services, traffic congestion, and excessive energy consumption
Since it is the main service that most other services within smart cities rely on, modeling road traffic flow is a key element as it has the potential to provide road users with vital information that can assist in making decisions, alleviating traffic congestion and improving the efficiency of transport system operations
We presented the generalized nondeterministic batch Petri nets model based on the intersection turning movement for road traffic flow prediction
Summary
The unprecedented growth of global urbanization has been accompanied by several critical issues such as air and noise pollution, insufficient public resource and services, traffic congestion, and excessive energy consumption. According to [1], 54% of the global population lives in urban land area, and it is projected to reach 70% by 2050. One of the most dramatic expressions of the urbanization phenomena can be found in [2], where the total economy-wide costs of congestion across four advanced economies, mainly the United States, the United Kingdom, France, and Germany, are estimated to soar from $200.7 billion in 2013 to $293.1 billion by 2030 (nearly a 50% increase from the 2013 gridlock costs). Smart cities can play a key role in facing the urbanization pressures and their associated issues [3,4,5] where information and communication technologies (ICTs) can be used while balancing the needs of present and future generations with respect to economic, social, and environmental aspects
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