Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes.     

A robust approach for road users classification using the motion cues

Video monitoring of traffic is a common practice in major cities. The data generated by video monitoring has practical uses such as traffic analysis for city planning. However, the usefulness of video monitoring of traffic is limited unless there is also a reliable way to automatically classify road users. This paper presents an automated method of road users’ classification into vehicles, cyclists, and pedestrians by using their motion cues. In this method, the movement of road users was captured on sequences of video frames. The videos were analysed using a feature-based tracking system, which has returned the tracks of road users. The separate pieces of information gained from these tracks are hereafter called Classifiers. There are nineteen classifiers included in this method. The classifiers’ values were assessed and integrated into a fuzzy membership framework, which in turn required prior configurations to be available. This led to the final classification of road users. The performance of this method demonstrated promising results. An important contribution of this paper is the creation of a robust approach that can integrate different classifiers using fuzzy membership framework. The developed method also uses parametric classifiers, which do not depend on the specific geometry or traffic operation of the intersection. This is a key advantage because it enables transferability and improves the practicality and usefulness of the method.     

Impact of public transport and non-motorized transport infrastructure on travel mode shares,energy, emissions and safety: Case of Indian cities

Current modal share in Indian cities is in favor of non-motorized transport (NMT) and public transport (PT), however historical trends shows decline in its use. Existing NMT and PT infrastructure in Indian cities is of poor quality resulting in increasing risk from road traffic crashes to these users. It is therefore likely that the current NMT and PT users will shift to personal motorized vehicles (PMV) as and when they can afford it. Share of NMT and PT users can be retained and possibly increased if safe and convenient facilities for them are created. This shall also have impact on reducing environment impacts of transport system.We have studied travel behavior of three medium size cities – Udaipur, Rajkot and Vishakhapatnam. Later the impact of improving built environment and infrastructure on travel mode shares, fuel consumption, emission levels and traffic safety in Rajkot and Vishakhapatnam are analyzed. For the purpose three scenarios are developed – improving only NMT infrastructure, improving only bus infrastructure and improving both NMT and bus infrastructure.The study shows the strong role of NMT infrastructure in both cities despite geographical dissimilarities. The scenario analysis shows maximum reduction in CO₂ emissions is achieved when both PT and NMT infrastructure are improved. Improvement in safety indicator is highest in this scenario. Improving only PT infrastructure may have marginal effect on overall reduction of CO₂ emissions and adverse effects on traffic safety. NMT infrastructure is crucial for maintaining the travel mode shares in favor of PT and NMT in future.     

轨道交通工程建设安全管理统一信息平台研究

结合目前轨道交通工程建设信息化应用程度和管理现状,本文提出一套界面友好.架构合理,业务信息共享,平台功能强大且实用的基于GIS安全管理统一信息平台.实现轨道交通工程建设业务信息资源深度集成和统一共享,全面提高业务信息化管理水平和安全风险处理能力.对信息化平台业务应用需求、关键技术路线、平台总体架构、业务数据流程及平台总体功能应用分别作了详细阐述,对于轨道交通工程信息化建设具有指导意义和应用价值.     

Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.     

Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals

As mobile traffic sensor technology gets more attention, mathematical models are being developed that utilize this new data type in various intelligent transportation systems applications. This study introduces simple analytical estimation models for queue lengths from tracked or probe vehicles at traffic signals using stochastic modeling approach. Developed models estimate cycle-to-cycle queue lengths by using primary parameters such as arrival rate, probe vehicle proportions, and signal phase durations. Valuable probability distributions and moment generating functions for probe information types are formulated. Fully analytical closed-form expressions are given for the case ignoring the overflow queue and approximation models are presented for the overflow case. Derived models are compared with the results from VISSIM-microscopic simulation. Analytical steady-state and cycle-to-cycle estimation errors are also derived. Numerical examples are shown for the errors of these estimators that change with probe vehicle market penetration levels, arrival rates, and volume-to-capacity ratios.     

Patterns and projections of technical development in freeway surveillance and control in the U.S.A.

This paper explores the historical trends in freeway traffic management technology in the U.S., and the most likely projections for the coming two decades. First, existing computer‐supervised freeway surveillance and control techniques are reviewed with particular emphasis on the scientific and technological landmarks which has led to the evolution of these techniques. Next, the major underlying trends which bear on the future of automated freeway surveillance and control are identified. Finally, extrapolative projections are made to determine the most likely future of this technology. The paper concludes with implications for the issues of meeting short‐term transportation needs of urban areas through more efficient use of existing transportation facilities.     

Book reviews

THE SEVENTH AUSTRALIAN TRANSPORT RESEARCH FORUM, by K. W. Ogden.

AIRLINE PLANNING: CORPORATE, FINANCIAL AND MARKETING by Nawal K. Taneja. Lexington, Mass., Lexington Books, 1982. 207 pp. (£19.50)

SOLVING LOCAL GOVERNMENT PROBLEMS: PRACTICAL APPLICATIONS OF OPERATIONS RESEARCH IN CITIES AND REGIONS, by Charles E. Pinkus and Anne Dixson. George Allen and Unwin, London, 1981. (Hardback £20.00; paperback £8.95)

SCIENTIFIC MANAGEMENT OF TRANSPORT SYSTEMS, edited by N. K. Jaismal. Published by North‐Holland Publishing Company (U.S. $58.50)

TRANSPORTATION AND TRAFFIC ENGINEERING HANDBOOK (Second Edition), edited by W. S. Homburger, Englewood Cliffs, N.J., 1982. Prentice Hall, pp. 883.

ECONOMICS OF PUBLIC TRANSPORT, by C. A. Nash. Longman, London, pp. 194. (£6.95).