A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions

Study on Traffic Status Threshold Based on Floating Taxi

AbstractThe applications of floating car in road traffic condition identification are taken seriously and gradually developed. The paper studies the variables threshold in traffic condition information based on the floating taxi: section traffic information update cycle, data sampling interval, section covering ratio, floating taxi sample size. The optimization idea of floating taxi sample size is given. The traffic condition identification algorithm based on the floating taxi is put forward. The practice in two road sections shows that the algorithm is feasible which can offer useful reference for urban traffic management and resident trips decision

Incorporation of micro-level analysis in strategic urban transport modelling: with a case study of the Greater Beijing

Many developing countries and regions are suffering from severe urban transport problems arising from accidents, congestion, air pollution, rising carbon intensity, and chronic under-funding of infrastructure and services. The problems make those cities the most polluted and often the least liveable. Strategic transport modelling has been recognised as an effective approach for developing and testing policy options, especially where it is integrated with land use planning and urban design. However, in most developing-country cities strategic transport modelling has been out of reach for practical policy use because of its sophisticated data and skill requirements, which currently imply unaffordable high costs and long durations for model development. This means that strategic urban transport modelling is the least available where it is needed most urgently. Meanwhile, the spread of smart data in mapping and urban activity monitoring has often been just as rapid in developing countries as in the developed. This has triggered new approaches in micro-level analyses of transport networks, personal movements and vehicles. In the most advanced cases, the new analyses have started to influence strategic modelling. The main hypothesis of this dissertation is that an incorporation of the micro-level smart data and analyses in strategic urban transport modelling will make it feasible to establish a sufficiently robust strategic transport model for evidence-based policy analysis with cost, time and skill thresholds that are close to being affordable in developing country cities. In order to test this main hypothesis, a number of novel model development tasks have been carried out which contribute to the field of applied urban modelling. This new approach aims to contribute to the transformation of the prevailing modus operandi where model development could not start in earnest until extensive data collection and skills training have been completed to a situation where a sufficiently robust model can be established cheaply and quickly to support on-going and incremental refinements. More specifically, new modelling tools have been developed as part of this dissertation using sparse GPS taxi traces to identify slow-moving and stopping traffic hotspots using an extended density-based spatial clustering algorithm that is tolerant of significant data noise, and to estimate congested road speeds (which used to be very costly and time-consuming to obtain if at all). The micro-level network, congested speeds and insights into the nature of the congested traffic have been incorporated into a MEPLAN-based strategic transport model interacting with a MEPLAN-based land use and travel demand model. This means that the strategic economic, social and environmental impacts of transport interventions can be tested in a robust way through accounting for the interactions among transport, land-use and background social-technical trends. A new approach to establish the medium to long term visions for alternative travel demand management and transport investment scenarios has been tested using this model. The methods and algorithms have been tested in a case study of the Greater Beijing region, which consists of the municipalities of Beijing and Tianjin together with the surrounding areas in the province of Hebei. The government’s data regulations of restricting overseas studies to using only publicly available data sources have made the case study ideal for testing the new approach. The potential of the new strategic urban transport model has been tested through a wide range of policy scenarios. The results suggest that the new approach developed in this dissertation has made it not only cheaper and faster to develop a robust model, but could also potentially fill a gap in the lack of medium to long term perspectives regarding major road and metro investments over the next two decades. Such analyses could be of critical importance in improving the performance of the transport system in terms of safety, economic efficiency, air quality and carbon reduction given the long lead times to plan and deliver transport infrastructure investments

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Impact on Network Performance of Probe Vehicle Data Usage: An Experimental Design for Simulation Assessment

Probe-based technologies are proliferating as a means of inferring traffic states. Technological companies are interested in traffic data for computing the best routes in a traffic-aware manner and they also provide real-time traffic information with certain temporal accuracy. This paper analyses and evaluates how data provided by a fleet of probe cars can be used to develop a navigation service and how the penetration rate of this service affects a set of city-scale KPIs (Key Performance Indicators) and driver KPIs. The case study adopts a model-driven approach in which microscopic simulation emulates real-size fleets of probe vehicles that provide positions and speed data. What is noteworthy about the modelling behaviour is that drivers are segmented according to their knowledge of network conditions for selected trips: experts, regular drivers, and tourists. The paper presents and discusses the modelling approach and the results obtained from an experimental Barcelona CBD model designed to evaluate the penetration rates of probe vehicles and route guidance. An analysis of the simulation experiments reveals remarkable links among city-scale KPIs, which—from a multivariate point of view—is a novelty. A simulation-based framework for results analysis and visualization is also introduced in order to simplify the simulation results analysis and easily visualize OD paths for driver segments.Throughout this work, the authors have benefited from the support of inLab FIB team at UPC and the suggestions of Jaume Barceló (Emeritus Professor at UPC). This research was funded by Spanish R+D Programs, TRA2016-76914-C3-1-P, and by Secretaria d’Universitats-i-Recerca-Generalitat de Catalunya, 2017-SGR-1749.Peer ReviewedPostprint (published version