Urban network traffic state estimation using a data-based approach

State estimation in urban traffic networks: A two-layer approach

Multi-level Model-based Control for Large Scale Urban Traffic Networks

The main objective of this project is to model and simulate a discrete based urban traffic network system. Nowadays, the congestion in traffic network is affecting the citizen’s standard of living especially for those who lives in urban area. This phenomenon cannot be avoided due to the increment of population in urban area. The effective and fast way to reduce this problem is by applying traffic lights optimiser at urban traffic network. Hence, high accuracy of traffic model is required to optimise the traffic lights signalisation. Cellular Automata is proposed to model the multiple intersections of urban traffic network system. Microscopic traffic network model is used to model the traffic flow of the traffic network. The vehicle parameters are determined for the computation of traffic condition. The purpose of applying these parameters is to estimate the traffic flow at the intersection more accurately. There are differences in the traffic flow when the vehicles are transferring from one intersection to another. The vehicles tend to decelerate for turning to left or right and accelerate for going straight. Besides, the probability of vehicles transferring to other lanes is required to model the real traffic network condition. Therefore, the modelling of the lane changing behaviour at the intersections has enhanced the precision of the developed model. Traffic light signalisation for the developed model is controlled by Petri Net. This traffic network model with consideration of more vehicle parameters such as time delay, maximum velocity, acceleration and deceleration has achieved a higher accuracy of traffic condition estimation.

Real-time traffic jams prediction inspired by Biham, Middleton and Levine (BML) model

With advances in information technologies, floating car data (FCD) (e.g. trajectories of taxis) becomes an important data source for collecting traffic information in urban road networks. Due to the low sampling frequency of FCD, it is a challenge to obtain accurate and reliable travel time estimation based on FCD. This paper presents a location-speed method for accurately estimating travel times in signalized road networks based on FCD. The proposed model utilizes trajectories of probe vehicles on the upstream and downstream of signalized intersections in order to estimate delays caused by traffic signals. A large-scale case study using real-world FCD in Wuhan is carried to validate the proposed method. The results of case study show that the proposed method can obtain satisfied estimation of travel times in signalized road networks.

A dynamic route optimization scheme based on short-term traffic flow prediction is designed. The overall idea of this paper is to use the real-time information of each section of the urban road communication network to predict the short-term traffic speed of the road in the next 5 min, in order to reflect the changes of the road traffic state of each section of the urban road traffic network. Then, the speed prediction information of each section in the urban road traffic network is converted into the estimated average travel time of each section by using the speed-time conversion formula, and then the road weight of each section in the urban road traffic network is set. Finally, the optimal route at the current time is calculated by using the improved algorithm based on the traditional Dijkstra algorithm. Before the traffic participant arrives at the destination, the scheme will cycle through the short-term prediction and route optimization of the traffic flow until the traffic participant arrives at the destination. And then achieve the balance of traffic conditions of various sections in the urban road traffic network, improve the situation of urban road traffic congestion, and avoid the emergence of bad road traffic conditions such as “navigation congestion”.

An Efficient Model for Urban Traffic Network Control

Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

Research on the Real-time Traffic Status Identification of Signalized Intersections Based on Floating Car Data

Surrogate-assisted cooperative signal optimization for large-scale traffic networks

Travel time is a basic measure based on which intelligent transportation systems such as traveler information systems, traffic management systems, public transportation systems are developed. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network is still an open problem that needs addressing. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate the growing number of cars and provide accurate information for routing. This study aims to address the aforementioned challenges by introducing a methodology, namely Similar Model Searching (SMS), to estimate travel times by using historical sparse travel time data. The SMS learns the temporal and spatial relationship between the travel time of adjacent links and utilize labeled data of similar models in order to improve its overall performance. The effectiveness of the proposed method is evaluated on a section of Leicestershire traffic network in the UK. The obtained results show that SMS efficiently estimates travel time of target links using models of adjacent traffic links.

Complex network theory is a multidisciplinary research direction of complexity science which has experienced a rapid surge of interest over the last two decades. Its applications in land-based urban traffic network studies have been fruitful, but have suffered from the lack of a systematic cognitive and integration framework. This paper reviews complex network theory related knowledge and discusses its applications in urban traffic network studies in several directions. This includes network representation methods, topological and geographical related studies, network communities mining, network robustness and vulnerability, big-data-based research, network optimization, co-evolution research and multilayer network theory related studies. Finally, new research directions are pointed out. With these efforts, this physics-based concept will be more easily and widely accepted by urban traffic network planners, designers, and other related scholars.

The improved degree of urban road traffic network: A case study of Xiamen, China

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

In this paper, we present the empirical investigation results on the urban public traffic networks (PTNs).We study four urban public traffic networks in south China, which are the urban public traffic networks of Guangzhou, Changsha, Zhuhai and Xiamin. The empirical results on the four big cities show that the accumulative act-degree distributions of PTNs take neither power function forms, nor exponential function forms, but they are described by two power piecewise function. We also study the accumulative act-size distributions of PTNs of the four cities, and they are described with a shifted Poisson distribution. In the end we shall explain the reason why the degree distribution of PTNs is difference from that of the other complex networks.