Modeling and simulation of network traffic flow evolution based on incomplete information feedback strategies in the ATIS environment

2017 ◽  
Vol 08 (03) ◽  
pp. 1750031
Author(s):  
Jianqiang Wang ◽  
Shiwei Li
Keyword(s):  
Incomplete Information ◽  
Travel Time ◽  
Traffic Flow ◽  
Network Traffic ◽  
Route Choice ◽  
Road Networks ◽  
Urban Traffic ◽  
Traffic Information ◽  
Information Feedback ◽  
Feedback Strategies

Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

Impact of information feedback strategy on the car accidents in two-route scenario

2018 ◽  
Vol 29 (09) ◽  
pp. 1850081 ◽  
Author(s):  
R. Marzoug ◽  
N. Lakouari ◽  
O. Oubram ◽  
H. Ez-Zahraouy ◽  
L. Cisneros-Villalobos ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Safety ◽  
Information Feedback ◽  
Dynamic Information ◽  
Feedback Strategy ◽  
Car Accidents ◽  
New Strategy ◽  
Feedback Strategies ◽  
The Impact

Various feedback strategies are proposed to improve the traffic flow. However, most of these works did not take road safety into consideration. In this paper, we studied the impact of four feedback strategies on the probability of rear-end collisions ([Formula: see text]). We proposed a new feedback strategy named Accidents Coefficient Feedback Strategy (ACFS) in which dynamic information can be generated and displayed on a board at the entrance of two-route scenario with intersection to help drivers to choose the appropriate road. This new strategy can greatly improve road safety and make the flow smooth as possible at the same time. Moreover, the impact of the intersection and boundary rates ([Formula: see text] and [Formula: see text]) on [Formula: see text] is also studied.

scholarly journals A Route Choice Model Considering Mean Travel Time and Its Variance on Road Networks

2010 ◽  
Vol 27 (0) ◽  
pp. 779-785 ◽  
Author(s):  
Naoki ANDO ◽  
Kazuki ARIMA ◽  
Yuki NAKAMURA ◽  
Tadashi YAMADA ◽  
Eiichi TANIGUCHI
Keyword(s):  
Travel Time ◽  
Choice Model ◽  
Route Choice ◽  
Road Networks

The impact of travelers’ rationality degree heterogeneity in the Advanced Traveler Information System on the network traffic flow evolution

SIMULATION ◽  
2017 ◽  
Vol 93 (6) ◽  
pp. 447-457 ◽  
Author(s):  
Jianqiang Wang ◽  
Shiwei Li
Keyword(s):  
Information System ◽  
Traffic Flow ◽  
Network Traffic ◽  
Mixed Logit ◽  
Evolution Model ◽  
Traffic Information ◽  
Traveler Information ◽  
Integrated Simulation ◽  
Advanced Traveler Information System ◽  
The Impact

The interplay between traffic information, which is normally distributed by the Advanced Traveler Information System (ATIS) and travelers’ decision behaviors, is prone to lead to high complexity in the evolution process of network traffic flow. Considering the obvious heterogeneity that is reflected in the numerous ways that travelers adopt ATIS information and choose routes, the lognormal distribution is adopted to describe the heterogeneity of travelers’ rationality degree. Introducing habitual factors of traveler route choice, modeling ideas of Multi-Agent and Mixed Logit are utilized to construct the day-to-day evolution model of network traffic flow, which is based on the value difference of travelers’ cognitive travel time. Furthermore, an integrated simulation algorithm based on the Monte Carlo method is specially designed to solve the previous evolution model. The simulation indicates that a lower individual difference and a higher rationality degree would lead to a more obvious aggregation phenomenon of network traffic flow and inefficiency of operation in road networks.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

2011 ◽  
Vol 97-98 ◽  
pp. 925-930
Author(s):  
Shi Xu Liu ◽  
Hong Zhi Guan
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Statistical Characteristics ◽  
Traffic Information ◽  
Route Choice Behavior ◽  
The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

Real-Time Monitoring Unit of Traffic Flow

2012 ◽  
Vol 588-589 ◽  
pp. 1058-1061
Author(s):  
Ting Zhang ◽  
Zhan Wei Song
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Traffic Information ◽  
Real Time Monitoring ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Transit Use ◽  
Control Traffic

With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

DINO: Roteamento Dinâmico para ITS Considerando Recursos da Rede

2021 ◽  
Author(s):  
Matheus Bernardino de Araújo ◽  
Matheus Monteiro Silveira ◽  
Rafael Lopes Gomes
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Network Flows ◽  
Urban Traffic ◽  
Traffic Information ◽  
Transport Systems ◽  
Packet Delivery ◽  
Vehicle Accidents ◽  
Average Travel Time ◽  
Definition Of

Intelligent Transport Systems (ITS) arose as a modern solution to traffic jams and vehicle accidents in the urban environment. A key part of the ITS is Traffic Management (TM), which concerns the planning and route definition of the vehicle. Existing TM solution focuses specifically on urban traffic information, ignoring the issues related to the network infrastructure and the applications at the top of it. Within this context, this paper presents a vehicle routing and re-routing strategy, called DINO, that considers both travel time of vehicles on the roads and the active network flows in the network, aiming to dynamically bring a suitable balance between travel time and packet delivery through a heuristic. The experiments performed suggest that DINO improves the packet delivery of the applications while reduces the average travel time of vehicles.

Continuous ACO in a SVR Traffic Forecasting Model

2011 ◽  
pp. 410-417
Author(s):  
Wei-Chiang Samuelson Hong
Keyword(s):  
Time Series ◽  
Travel Time ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Forecasting Model ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Traffic Forecasting ◽  
Proposed Model ◽  
Fuel Costs

The effective capacity of inter-urban motorway networks is an essential component of traffic control and information systems, particularly during periods of daily peak flow. However, slightly inaccurate capacity predictions can lead to congestion that has huge social costs in terms of travel time, fuel costs and environment pollution. Therefore, accurate forecasting of the traffic flow during peak periods could possibly avoid or at least reduce congestion. Additionally, accurate traffic forecasting can prevent the traffic congestion as well as reduce travel time, fuel costs and pollution. However, the information of inter-urban traffic presents a challenging situation; thus, the traffic flow forecasting involves a rather complex nonlinear data pattern and unforeseen physical factors associated with road traffic situations. Artificial neural networks (ANNs) are attracting attention to forecast traffic flow due to their general nonlinear mapping capabilities of forecasting. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training errors. SVR has been used to deal with nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines SVR model with continuous ant colony optimization (SVRCACO), to forecast inter-urban traffic flow. A numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed model. The simulation results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time-series model.

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