Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Geomatics for Mobility Management. A comprehensive database model for Mobility Management

In urban and metropolitan context, Traffic Operations Centres (TOCs) use technologies as Geographic Information Systems (GIS) and Intelligent Transport Systems (ITS) to tackling urban mobility issue. Usually in TOCs, various isolated systems are maintained in parallel (stored in different databases), and data comes from different sources: a challenge in transport management is to transfer disparate data into a unified data management system that preserves access to legacy data, allowing multi-thematic analysis. This need of integration between systems is important for a wise policy decisions. This study aims to design a comprehensive and general spatial data model that could allow the integration and visualization of traffic components and measures. The activity is focused on the case study of 5T Agency in Turin, a TOC that manages traffic regulation, public transit fleets and information to users, in the metropolitan area of Turin and Piedmont Region. In particular, the agency has set up during years a wide system of ITS technologies that acquires continuously measures and traffic information, which are used to deploy information services to citizens and public administrations. However, the spatial nature of these data is not fully considered in the daily operational activity, with the result of difficulties in information integration. Indeed the agency lacks of a complete GIS that includes all the management information in an organized spatial and “horizontal” vision. The main research question concerns the integration of different kind of data in a unique GIS spatial data model. Spatial data interoperability is critical and particularly challenging because geographic data definition in legacy database can vary widely: different data format and standards, data inconsistencies, different spatial and temporal granularities, different methods and enforcing rules that relates measures, events and physical infrastructures. The idea is not to replace the existing implemented and efficient system, but to built-up on these systems a GIS that overpass the different software and DBMS platforms and that can demonstrate how a spatial and horizontal vision in tackling urban mobility issues may be useful for policy and strategies decisions. The modelling activity take reference from a transport standards review and results in database general schema, which can be reused by other TOCs in their activities, helping the integration and coordination between different TOCs. The final output of the research is an ArcGIS geodatabase, tailored on 5T data requirements, which enable the customised representation of private traffic elements and measures. Specific custom scripts have been developed to allow the extraction and the temporal aggregation of traffic measures and events. The solution proposed allows the reuse of data and measures for custom purposes, without the need to deeply know the entire ITS environment system. In addition, The proposed ArcGIS geodatabase solution is optimised for limited power-computing environment. A case study has been deepened in order to evaluate the suitability of the database: a confrontation between damages, detected by Emergency Mapping Services (EMS), and Traffic Message Channel traffic events, has been conducted, evaluating the utility of 5T historical information of traffic events of the Piedmont floods of November 2016 for EMS services

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Traffic Time Headway Prediction and Analysis: A Deep Learning Approach

In the modern world of Intelligent Transportation System (ITS), time headway is a key traffic flow parameter affecting ITS operations and planning. Defined as “the time difference between any two successive vehicles when they cross a given point”, time headway is used in various traffic and transportation engineering research domains, such as capacity analysis, safety studies, car-following, and lane-changing behavior modeling, and level of service evaluation describing stochastic features of traffic flow. Advanced travel and headway information can also help road users avoid traffic congestion through dynamic route planning, for instance. Hence, it is crucial to accurately model headway distribution patterns for the purpose of analyzing traffic operations and making subsequent infrastructure-related decisions. Previous studies have applied a variety of probabilistic models, machine learning algorithms (for example, support vector machine, relevance vector machine, etc.), and neural networks for short-term headway prediction. Recently, deep learning has become increasingly popular following a surge of traffic big data with high resolution, thriving algorithms, and evolved computational capacity. However, only a few studies have exploited this emerging technology for headway prediction applications. This is largely due to the difficulty in capturing the random, seasonal, nonlinear, and spatiotemporal correlated nature of traffic data and asymmetric human driving behavior which has a significant impact on headway. This study employs a novel architecture of deep neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamics effectively to predict vehicle headway. LSTM NN can overcome the issue of back-propagated error decay (that is, vanishing gradient problem) existing in regular Recurrent Neural Network (RNN) through memory blocks which is its special feature, and thus exhibits superior capability for time series prediction with long temporal dependency. There is no existing appropriate model for long term prediction of traffic headway, as existing models lack using big dataset and solving the vanishing gradient problem because of not having a memory block. To overcome these critics and fill the gaps in previous works, multiple LSTM layers are stacked to incorporate temporal information. For model training and validation, this study used the USDOT’s Next Generation Simulation (NGSIM) dataset, which contains historical data of some important features to describe the headway distribution such as lane numbers, microscopic traffic flow parameters, vehicle and road shape, vehicle type, and velocity. LSTM NN can capture the historical relationships between these variables and save them using its unique memory block. At the headway prediction stage, the related spatiotemporal features from the dataset (HighwayI-80) were fed into a fully connected layer and again tested with testing data for validation (both highway I-80 & US 101). The predicted accuracy outperforms previous time headway predictions

A system approach on safe emergency evacuation in Subways: A systematic literature review

Background: Due to the extensive use of subway transportation in high- and middle-income countries, the safety of passengers has become one of the important challenges in emergency management of subway station. Therefore, the present systematic review aimed to identify environmental and organizational management factors that affect the safe emergency evacuation in subway stations. Materials and Methods: In this systematic literature review, PubMed, Scopus, Web of Science, ProQuest, Google Scholar, Iran Medex, Magiran, and Scientific Information Database from 1990 to 2019 were searched to identify effective emergency management factors in safe emergency evacuation of the subways. A thematic content analysis was employed for data analysis. Results: Of 763 publications retrieved from the searches, 149 studies were included for data analysis. According to the findings, effective environmental and organizational management factors in safe emergency evacuation were discussed in eight subcategories, including infrastructure properties, evacuation-assisting resources, prevention of injuries and mitigation, preparedness for emergency evacuation, emergency response and reconstruction, and maintenance of evacuation facilities. Conclusion: The design of an optimal route for emergency evacuation is the main theme of most studies focusing on environmental factors. While a system approach for designer is needed for effective subway emergency evacuation, human-related factors focusing on injury prevention are also crucial

Literature Review of Papers relevant to the topic of development impacts and economic evaluation methods of High-Speed Rail (HSR)

With HSR continuing to the target of investments around the world, with even the United States elevating the place of HSR on the public agenda, we thought this compendium of summaries of references on HSR and related topics would be of value. We begin with references on transportation investment and economic development in general. Then we consider the case of HSR and economic development on the local and urban as well as the national regional levels, Some references on economic geography and disparities among regions are included. We conclude with references for demand forecasting and some general references in the HSR field

BIDIRECTIONAL LSTM AND KALMAN FILTER FOR PASSENGER FLOW PREDICTION ON BUS TRANSPORTATION SYSTEMS

Forecasting travel demand is a complex problem facing public transit operators. Passenger flow prediction is useful not only for operators, used for long-term planning and scheduling, but also for transit users. The time is quickly approaching that short-term passenger flow prediction will be expected as a matter of course by transit users. To address this expectation, a Bi-directional Long Short-Term Memory Neural Network model (BDLSTM NN) and a Bi-directional Long Short-Term Memory Neural Network Kalman Filter model (BDLSTM KF) predict short-term passenger flow and based on the dependencies between passenger count and spatial-temporal features. A comprehensive preprocessing framework is proposed leveraging historical data and extracting bidirectional features of passenger flow. The proposed model is based on [1] but adapted, applied, and analysed to produce optimal results for passenger flow forecasting on a bus route. Building on [2], a BDLSTM architecture is then combined with a Kalman filter. The Kalman filter reduces the training and testing complexity required for passenger flow forecasting. The BDLSTM-based Kalman filter produces predictions with less uncertainty than each method alone. Evaluating the BDLSTM-based Kalman filter with two months of real-world data, one year apart shows positive improvements for short-term forecasting in high complexity bus networks. It is possible to see that the BDLSTM outperforms traditional machine and deep learning techniques used in this context