Traffic Congestion In Big Cities Research Articles

Exploring Spatiotemporal Patterns of Frequently Congested Urban Road Segments Based on Multi-Source Data: A Case Study of China’s Super-Large Cities

Traffic congestion has emerged as a primary obstacle to sustainable urban development and the livability of life. Seven super-large cities in China were the study area of this paper as the problem tends to be more severe in larger cities. First, frequently congested road segments were defined based on real-time traffic condition data from online maps. Then, the spatiotemporal characteristics of these road segments were explored. Finally, mitigation measures and ways to ease traffic congestion in big cities were discussed against the background of rapid urbanization. The results demonstrate that the proportion of frequently congested road segments takes up between 1.8% and 9.87% of China’s super-large cities. There is a positive correlation between the overall operational efficiency of the urban road network and the ratio of frequently congested road segments. Temporally, commuting patterns during the morning and evening are displayed by these road segments. Spatially, they also demonstrate significant clustering characteristics. The proportion of frequently congested road segments caused by the lack of alternative routes is about 20% in the six cities studied. The data for Chongqing is even higher at 42.94%. Therefore, congestion caused by the lack of alternative routes may be underestimated by existing research. This work could provide theoretical support for transportation planning and management. As cities continue to expand and urbanization accelerates, the significance of this research will increase.

Research on the Development Evaluation Method of TOD Development of Metro Station Domain

The public transport-oriented TOD model is a sustainable planning concept currently being implemented. It's in the TOD area. Most destinations can be reached directly by public transport. This emphasizes the attractiveness of public transport for residents to travel and reduces people's over-reliance on private cars, thereby improving urban problems such as diseases and traffic congestion in big cities. However, at present, the evaluation methods of TOD development effectiveness at home and abroad are insufficient in pertinence and timeliness, and can not fully evaluate the development effectiveness of TOD in orbital station area. This article takes the Beijing Metro Railway Station as an example to carry out the effective evaluation method of TOD development in the rail station domain. This paper tries to put forward a set of practical and effective evaluation methods for the development of orbital station TOD in Beijing. Therefore, it provides technical support for the further promotion of TOD development mode in the process of new city planning and old city renewal in Beijing.

SCATS (Sydney Coordinated Adaptive Traffic System) As A Solution To Overcome Traffic Congestion in Big Cities

The development of an urban area should be followed by the arrangement of adequate infrastructure. One of the important needs in an urban area is transportation. An increase in the number of means of transport often creates congestion, especially at intersections. The timing of cycles that do not lead tothe number of vehicles lining up but only based on a balanced division of time is one of the causes of congestion. Based on this background, a system of setting the flame cycle of the lamp u basedon the length of the queue is needed. This study aims to implement the Sydney Coordinat Adaptive Traffic System (SCATS) application as a solution to overcome traffic congestion in DKI Jakarta. The method used is to use quantitative methods with the 1997 Indonesian Road Capacity Manual (MKJI) approach. The results obtained include, the SCATS application can adaptively adjust the cycle time of sensor-based traffic lights. Traffictraffic settings can be manually controlled by the operator remotely using an internet communication network and can be conditioned with special cases or emergencies. Based on the results of the study, the SCATS application is expected to be implemented in all major cities in Indonesia.

Parametric Modelling of Pedal Pressing Activities During Road Traffic Delay

Abstract: Traffic congestion in big cities in Malaysia has become a common scenario among the communities. The journey between homes to working place twice a day at considerable distances is no longer a strange situation. Being in traffic for hours in a sitting position requires recurrent tasks of manual pressing the pedal and brake excessively and if they are done without the correct sitting posture, it may trigger fatigue faster, particularly for the leg and back of the driver. In the long term, it will negatively affect the health of the driver, particularly in the form of physical, psychological, and emotional. Therefore, this paper is trying to investigate the recurrent brake pedal pressings as well as the leg postures while driving in traffic jam. The research is started with the experimental setup and data acquisition on brake pedal pressing as well as leg posture followed by the modelling and analysis of the obtained data using particle swarm optimization (PSO) modelling technique. The validation step was then executed to verify the model derived using open loop and closed loop performance analysis. The results show that the pedal pressing force of leg posture can be closely represented using 2ndorder transfer function and mimics the actual pedal pressing pattern during road traffic delay.

Четыре колеса апокалипсиса: причины автомобильных пробок в крупных городах мира

Четыре колеса апокалипсиса: причины автомобильных пробок в крупных городах мира

Urban Traffic Route Guidance Method With High Adaptive Learning Ability Under Diverse Traffic Scenarios

With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance promises to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city implies that traffic scenarios in real systems are diverse. Therefore, if a route guidance method cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee optimal results under all traffic scenarios. Thus, ideal dynamic route guidance methods should have a highly adaptive learning ability under diverse traffic scenarios so as to have extensive improvement capabilities for different traffic scenarios. In this study, an A* trajectory rejection method based on multi-agent reinforcement learning (A*R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) is proposed; the method integrates both system and user perspectives to mitigate traffic congestion and reduce travel time (TT) and travel distance (TD). First, owing to its adaptive learning ability, the A*R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can comprehensively analyze the traffic conditions for different traffic scenarios and intelligently evaluate the road congestion index from a system perspective. Then, the A*R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> determines the routes for all vehicles from user perspective according to the road network congestion index. An extensive set of simulation experiments reveal that, under various traffic scenarios, the A*R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can rely on its adaptive learning ability to achieve better traffic efficiency. Moreover, even in cases where many drivers are not fully compliant with the route guidance, the traffic efficiency can still be improved significantly by A*R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Statistical analysis of the stability of bus vehicles based on GPS trajectory data

High quality bus service is considered as an efficient way to mitigate traffic congestion in big cities. Global positioning system (GPS) data provide sufficient sources to evaluate the performance of bus vehicles that both passengers and operator concern about. This paper aims to propose a framework to assess the operational performance of bus routes based on the GPS trajectory data collected from Jinan, China. Several important indicators of bus operation including travel time of routes, section running time, dwell time and bus bunching have been studied. The results show that the travel time of routes follow right skewed distributions. Moreover, section running time between two consecutive stations varies in different time period and it is larger in evening peak hours. Additionally, the dwell time has been discussed and the results show that there is no big variation in most stations except some stations, which provides a help to identify the key stations. Furthermore, we propose an approach to detect the bunching points. The results indicate the bunching points are easy to occur in the peak hours and the congested road section.

Empirical Study on Maximum Traffic Throughputs at Intersections

Traffic congestion in big cities of Thailand has been a major problem seriously deterring economy growth. An average travel speed has become lower than 15 km/hour during peak-hour periods. One of the current major questions is how to find the most optimum green time that minimizes vehicle delay at an intersection. Since the identification of this optimum green time significantly depends on the knowledge of how fast vehicles can pass through an intersection, the information of the maximum traffic throughputs at intersections at different locations is indispensable. In this study, therefore, traffic counts were extracted manually from the videos recorded at 4 major intersections in Saensuk city. After the data were analyzed, the maximum traffic throughputs for different lane numbers and configurations were calculated and summarized. A better understanding of this maximum traffic throughput at intersections will lead to more accurate estimation of optimal green time.

Analysis and Prediction of Urban Traffic Congestion Based on Big Data

With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.

Bi-objective nonlinear programming with minimum energy consumption and passenger waiting time for metro systems, based on the real-world smart-card data

ABSTRACTMetro is considered as an efficient transport mode to alleviate traffic congestion in big cities because of its large transport capacity. Generally, a good metro system means not only a passenger-oriented timetable but also an eco-friendly speed profile. This study develops a bi-objective nonlinear programming model to determine the optimal timetable and speed profile, with minimum energy consumption and passenger waiting time. In the nonlinear formulation, the average passenger waiting time is calculated based on the dynamic passenger flow by using the real-world smart-card data, and the energy consumption is obtained based on the tractive and regenerative energy on each section. The high-dimensional nonlinear problem is converted to a classical quadratic programming by using the Taylor approximation for obtaining the optimal solution easily. Finally, we conduct a numerical example based on the real-world data from the Beijing Metro Yizhuang Line of China. The results show that the developed model can save energy consumption by 6.0% and reduce passenger waiting time by 10.9% in comparison with the current planned timetable.

Two-Level Hierarchical Model-Based Predictive Control for Large-Scale Urban Traffic Networks

Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g., computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand-balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies.