Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations.

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Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.

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Review of road traffic control strategies

Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work.

Short-term traffic forecasting: Where we are and where we’re going

Accurate short-term traffic flow forecasting has become a crucial step in the overall goal of better road network management. Previous research [H. Kirby, M. Dougherty, S. Watson, Should we use neural networks or statistical models for short term motorway traffic forecasting, International Journal of Forecasting 13 (1997) 43–50.] has demonstrated that a straightforward application of neural networks can be used to forecast traffic flows along a motorway link. The objective of this paper is to report on the application and performance of an alternative neural computing algorithm which involves ‘sequential or dynamic learning’ of the traffic flow process. Our initial work [H. Chen, S. Clark, M.S. Dougherty, S.M. Grant-Muller, Investigation of network performance prediction, Report on Dynamic Neural Network and Performance Indicator development, Institute for Transport Studies, University of Leeds Technical Note 418, 1998 (unpublished)] was based on simulated data (generated using a Hermite polynomial with random noise) that had a profile similar to that of traffic flows in real data. This indicated the potential suitability of dynamic neural networks with traffic flow data. Using the Kalman filter type network an initial application with M25 motorway flow data suggested that a percentage absolute error (PAE) of approximately 9.5% could be achieved for a network with five hidden units (compared with 11% for the static neural network model). Three different neural networks were trained with all the data (containing an unknown number of incidents) and secondly using data wholly obtained around incidents. Results showed that from the three different models, the ‘simple dynamic model’ with the first five units fixed (and subsequent hidden units distributed amongst these) had the best forecasting performance. Comparisons were also made of the networks’ performance on data obtained around incidents. More detailed analysis of how the performance of the three networks changed through a single day (including an incident) showed that the simple dynamic model again outperformed the other two networks in all time periods. The use of ‘piecewise’ models (i.e. where a different model is selected according to traffic flow conditions) for data obtained around incidents highlighted good performance again by the simple dynamic network. This outperformed the standard Kalman filter neural network for a medium-sized network and is our overall recommendation for any future application.

Use of sequential learning for short-term traffic flow forecasting

This paper describes an application of hybrid neural network approaches and an assessment of the effects of missing data on highway traffic flow forecasting. Using a self-organizing map (SOM), two hybrid approaches are developed for classifying traffic into different states. In the first hybrid approach, four auto-regressive integrated moving average (ARIMA) models are included. The second approach uses two multi-layer perception (MLP) models. The effects of missing data on neural network performance when forecasting traffic flow are analyzed, and options to replace the missing data are discussed. It is concluded that overall, ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.

A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting

With the increasing number of vehicle and traffic jams, urban traffic management is becoming a serious issue. In this article, we propose novel four-tier architecture for urban traffic management with the convergence of VANETs, 5G networks, software-defined networks, and mobile edge computing technologies. The proposed architecture provides better communication and more rapid responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly shorten the rescue time. Key technologies with respect to vehicle localization, data pre-fetching, traffic lights control, and traffic prediction are also discussed. Obviously, the novel architecture shows noteworthy potential for alleviating traffic congestion and improving the efficiency of urban traffic management.

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High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication

Fuel consumption and exhaust emissions of diesel vehicles in worldwide harmonized light vehicles test cycles and their sensitivities to eco-driving factors

Nowadays urban pollution exposure from road transport has become a great concern in major cities throughout the world. A modelling framework has been developed to simulate Personal Exposure Frequency Distributions (PEFDs) as a function of urban background and roadside pollutant concentrations, under different traffic conditions. In this paper, we present a technique for classifying roads, according to their traffic conditions, using the traffic characteristics and fleet compositions. The pollutant concentrations data for 2001 from 10 Roadside Pollution Monitoring (RPM) units in the city of Leicester were analysed to understand the spatial and temporal variability of the pollutant concentrations patterns. It was found that variability of pollutants during the day can be associated with specific road traffic conditions. Statistical analysis of two urban and two rural Automated Urban and Rural Network (AURN) background sites for particulates suggests that PM"2"."5 and PM"1"0 are closely related at urban sites but not at rural sites. The ratio of the two pollutants observed at Marylebone was found to be 0.748, which was applied to Leicester PM"1"0 data to obtain PM"2"."5 profiles. These results are being used as an element in the PEFDs model to estimate the impact of urban traffic on exposure.

Haibo Chen

Papers

Use of sequential learning for short-term traffic flow forecasting

A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting

High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication

Fuel consumption and exhaust emissions of diesel vehicles in worldwide harmonized light vehicles test cycles and their sensitivities to eco-driving factors

Classification of road traffic and roadside pollution concentrations for assessment of personal exposure