Intelligent Planning and Research on Urban Traffic Congestion

For large and medium-sized cities, the planning and development of urban road networks may not keep pace with the growth of urban vehicles, resulting in traffic congestion on urban roads during peak hours. Take Jinan, a mid-sized city in China’s Shandong Province, for example. In view of the daily traffic jam of the city’s road traffic, through investigation and analysis, the existing problems of the road traffic are found out. Based on real-time, daily road traffic data, combined with the existing road network and the planned road network, the application of a road intelligent transportation system is proposed. Combined with the application of a road intelligent transportation system, this paper discusses the future development of urban road traffic and puts forward improvement suggestions for road traffic planning. This paper has reference value for city development, road network construction, the application of intelligent transportation systems, and road traffic planning.

[1]  Kong Ling-bin.  Discussion on"Technical Standards of Traffic Impact Analysis of Construction Projects" , 2010 .

[2]  Jing Tian,et al.  An ArcMap plug-in for calculating landscape metrics of vector data , 2019, Ecol. Informatics.

[3]  Alessandro Severino,et al.  Smart Roads: An Overview of What Future Mobility Will Look Like , 2020, Infrastructures.

[4]  E. Macioszek,et al.  The Analysis of the Factors Determining the Choice of Park and Ride Facility Using a Multinomial Logit Model , 2021, Energies.

[5]  M. Jacyna,et al.  The Assessment of Energy Efficiency versus Planning of Rail Freight Traffic: A Case Study on the Example of Poland , 2021, Energies.

[6]  Yifan Liu,et al.  High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting , 2021, ISPRS Int. J. Geo Inf..

[7]  Serge P. Hoogendoorn,et al.  A macroscopic flow model for mixed bicycle–car traffic , 2020, Transportmetrica A: Transport Science.

[8]  Haibo Zhang,et al.  On-line Simulation System of Urban Road Traffic Signal Control Based on Scene Driven , 2019, 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS).

[9]  Reza Langari,et al.  Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples , 2021, Energies.

[10]  Jun Tanimoto,et al.  Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System , 2019, Chaos, Solitons & Fractals.

[11]  C. M. Sperberg-McQueen,et al.  eXtensible Markup Language (XML) 1.0 (Second Edition) , 2000 .

[12]  D. A. Kagiri,et al.  Factors Affecting Completion of Road Construction Projects in Nairobi City County : Case Study of Kenya Urban Roads Authority ( KURA ) , 2015 .

[13]  Laila Benhlima,et al.  Petri net extension for traffic road modelling , 2016, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

[14]  Yijun Yu,et al.  An Open Framework for Semantic Code Queries on Heterogeneous Repositories , 2015, 2015 International Symposium on Theoretical Aspects of Software Engineering.

[15]  Yujia Zhao,et al.  Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction , 2021, Applied Sciences.

[16]  Agata Kurek,et al.  The Use of a Park and Ride System—A Case Study Based on the City of Cracow (Poland) , 2020, Energies.

[17]  Jingwei Ge,et al.  Simulation of non-motor vehicle and pedestrian mixed crossing behavior , 2021, Journal of Physics: Conference Series.

[18]  L.F.M. Sanchez,et al.  Condition assessment of an ASR-affected overpass after nearly 50 years in service , 2020 .

[19]  Alois Knoll,et al.  Graph Neural Networks for Modelling Traffic Participant Interaction , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[20]  D. Zhuang,et al.  Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing. , 2017, The Science of the total environment.

[21]  B. Kerner,et al.  Probabilistic Breakdown Phenomenon at On-Ramp Bottlenecks in Three-Phase Traffic Theory , 2006 .

[22]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[23]  Changle Li,et al.  Urban Traffic Bottleneck Identification Based on Congestion Propagation , 2018, 2018 IEEE International Conference on Communications (ICC).

[24]  Gang Lu,et al.  Exploration of Internal and External Factors of Swimmers’ Performance Based on Biofluid Mechanics and Computer Simulation , 2021, International journal of environmental research and public health.

[25]  Yixia Liu.  The Modeling and Simulation of the Tandem Intersection Considering the Vehicle Operation Law , 2020 .

[26]  Tamás Péter,et al.  A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System , 2021, Sustainability.

[27]  Yongmei Zhao,et al.  Improving the approaches of traffic demand forecasting in the big data era , 2018, Cities.

[28]  Houbing Song,et al.  Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city , 2017, Future Gener. Comput. Syst..

[29]  Chaoyang Wu,et al.  Review of Ramp Metering Methodologies for Urban Expressway , 2019 .

[30]  Huan Wang,et al.  An urban traffic simulation model for traffic congestion predicting and avoiding , 2016, Neural Computing and Applications.

[31]  D. Geneletti,et al.  A performance-based planning approach integrating supply and demand of urban ecosystem services , 2020, Landscape and Urban Planning.

[32]  Nitin Rakesh,et al.  Smart City Traffic Control System , 2019 .

[33]  Yuan Zheng,et al.  Numerical simulation of transient flow in a shaft extension tubular pump unit during runaway process caused by power failure , 2020 .

[34]  Ka Lok Man,et al.  Using Blockchain to Enhance and Optimize IoT-based Intelligent Traffic System , 2019, 2019 International Conference on Platform Technology and Service (PlatCon).

[35]  Yuan Zheng,et al.  Numerical study of turbulent flow past a rotating axial-flow pump based on a level-set immersed boundary method , 2021 .

[37]  Zhengyi Ge.  Reinforcement Learning-based Signal Control Strategies to Improve Travel Efficiency at Urban Intersection , 2020, 2020 International Conference on Urban Engineering and Management Science (ICUEMS).

[38]  Ziyou Gao,et al.  Crowded urban traffic: co-evolution among land development, population, roads and vehicle ownership , 2019, Nonlinear dynamics.

[39]  Domokos Esztergár-Kiss,et al.  The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time , 2021, Energies.

[40]  Julio A. Sanguesa,et al.  Advances in smart roads for future smart cities , 2020, Proceedings of the Royal Society A.

[41]  Dan Liu,et al.  City size, migration and urban inequality in China , 2018, China Economic Review.

[42]  Mahesh Kumar Chopker.  Product Discovery over HTTP Interface via Web Finger Printing , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[43]  S. Mitra,et al.  Why do they live so far from work? Determinants of long-distance commuting in California , 2019, Journal of Transport Geography.

[44]  Yuan Zheng,et al.  Transient characteristics during power-off process in a shaft extension tubular pump by using a suitable numerical model , 2021 .