Prediction Model of Urban Traffic Performance Index Using ARIMAX

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Qing He ◽  
Yong-Shen Chen ◽  
Jun Qiao ◽  
Jian-Dong Qiu ◽  
Yang Li
Keyword(s):  
Prediction Model ◽  
Performance Index ◽  
Urban Traffic ◽  
Traffic Performance

Study on Extract and Repair Traffic Performance Index Data of Beijing

2014 ◽  
Vol 1073-1076 ◽  
pp. 2443-2446
Author(s):  
Fan Zhang
Keyword(s):  
Performance Index ◽  
Local Authority ◽  
Urban Traffic ◽  
Traffic Pattern ◽  
Urban Road ◽  
Local Database ◽  
Index Data ◽  
Traffic Performance ◽  
Data Extract ◽  
Data Missing

Urban traffic expert developed many methods to judge the performance of urban road to provide driving instruction to both drivers and the local authority. In Beijing, the traffic performance index is widely used by people every day. In order to store the traffic performance index data which is continually changed to local database, our team has tried many different ways to achieve it because we want to do deep analyze of it. By using VBA programming we successfully build a data extract system to store data. We use two different ways to process the data from the system based on data missing type. At last, the data is ready for further study to find the traffic pattern of urban area.

scholarly journals Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fengkai Liu ◽  
Xingmin Ma ◽  
Xingshuo An ◽  
Guangnan Liang
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Support Vector ◽  
Swarm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Chaotic Particle Swarm Optimization ◽  
Smooth Support Vector Machine

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

scholarly journals The spatial variability of vehicle densities as determinant of urban network capacity

2010 ◽  
Vol 368 (1928) ◽  
pp. 4627-4647 ◽  
Author(s):  
Amin Mazloumian ◽  
Nikolas Geroliminis ◽  
Dirk Helbing
Keyword(s):  
Fluid Dynamic ◽  
Analytical Formula ◽  
Network Capacity ◽  
Average Density ◽  
Urban Traffic ◽  
Flow Measurements ◽  
Urban Network ◽  
The Road ◽  
Simple Simulation ◽  
Traffic Performance

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far have been based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula.

scholarly journals Macroscopic Modeling of On-Street and Garage Parking: Impact on Traffic Performance

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Manuel Jakob ◽  
Monica Menendez
Keyword(s):  
Urban Traffic ◽  
Short Term ◽  
Macroscopic Modeling ◽  
Aggregated Data ◽  
City Councils ◽  
Traffic Performance ◽  
Numerical Solver ◽  
Parking Pricing ◽  
The City ◽  
Over Time

The short-term interactions between on-street and garage parking policies and the associated parking pricing can be highly influential to the searching-for-parking traffic and the overall traffic performance in the network. In this paper, we develop a macroscopic on-street and garage parking decision model and integrate it into a traffic system with an on-street and garage parking search model over time. We formulate an on-street and garage parking-state-based matrix that describes the system dynamics of urban traffic based on different parking-related states and the number of vehicles that transition through each state in a time slice. This macroscopic modeling approach is based on aggregated data at the network level over time. This leads to data collection savings and a reduction in computational costs compared to most of the existing parking/traffic models. This easy to implement methodology can be solved with a simple numerical solver. All parking searchers face the decision to drive to a parking garage or to search for an on-street parking space in the network. This decision is affected by several parameters including the on-street and garage parking fees. Our model provides a preliminary idea for city councils regarding the short-term impacts of on-street and garage parking policies (e.g., converting on-street parking to garage parking spaces, availability of garage usage information to all drivers) and parking pricing policies on: searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental impact), as well as the revenue created for the city by the hourly on-street and garage parking fee rates. This model can be used to analyze how on-street and garage parking policies can affect traffic performance; and how traffic performance can affect the decision to use on-street or garage parking. The proposed methodology is illustrated with a case study of an area within the city of Zurich, Switzerland.

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