Estimating Path Choice Models through Floating Car Data

The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts

Forecasting Delivery Pattern through Floating Car Data: Empirical Evidence

This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability

Bus travel time: experimental evidence and forecasting

Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods

A modelling system to link end-consumers and distribution logistics

In the last years the interest in urban freight mobility has increased. However, the management and control of urban freight transport requires models which simulate the transport system. In literature some models have been analysed and implemented with tools which allow the verification of the measures adopted in several cities around the world. In paper a review of measures implemented in some cities to reduce the negative effects of urban freight transport, an updated review of models developed to analyse urban freight mobility and the tools used to verify and check the proposed measures are presented. Finally the modelling system to link endconsumer and distribution logistics is described

Sustainable urban delivery: the learning process of path costs enhanced by information and communication technologies

Today, local administrations are faced with the presence of greater constraints in terms of the use of space and time. At the same time, large amount of data is available to fleet managers that can be used for controlling their fleets. This work is set in the context defined by sustainable city logistics, and information and communication technologies (ICTs), to formalize the three themes of the smart city (transport, ICTs and energy savings) in a single problem. Following this, the main purpose of the study is to propose a unified formulation of the basic problem of fleets, i.e., the traveling salesman problem (TSP), which explicitly includes the use of emerging information and communication technologies (e-ICTs) pointing out the learning process of path costs in urban delivery. This research explores the opportunity to extend the path cost formation with a within-day and day-to-day learning process, including the specification of the attributes provided by e-ICTs. As shown through a real test case, the research answers to queries coming from operators and collectivities to improve city liveability and sustainability. It includes both economic sustainability for companies/enterprises and environmental sustainability for local administrations (and collectivities). Besides contributing to reduce the times and kms travelled by commercial vehicles, as well as the interference of freight vehicles with other traffic components, it also contributes to road accident reduction (social sustainability). Therefore, after the re-exanimation of TSP, this paper presents the proposed unitary formulation and its benefits through the discussion of results obtained in a real case study. Finally, the possible innovation guided by e-ICT is pointed out

Restocking in Touristic and CBD Areas: Deterministic and Stochastic Behaviour in the Decision-making Process

AbstractThe paper examines urban activity restocking process. The proposed models aim at examining how city logistics measures could modify the restocking process of retailers and ho.re.ca. managers located within the urban area. The process is considered in terms of distribution channel: pull or push movements to bring freight to the economic activities. The analysis has been based on surveys carried out in the inner area of Rome. The study points out that deterministic behaviour exists in relation to goods types and that the choices for acquisition (i.e. distribution channel and restocking area) are generally joint choices. For this scope, different behavioural models were tested according to different hypothesis on random residual distributions

A trip chain order model for simulating urban freight restocking

This paper proposes a trip chain order model for simulating retailer restocking within urban and metropolitan area. It is part of the general modelling system developed by the authors for simulating urban freight demand which considers both demand and logistic sub-systems. The former allows us to obtain the freight Origin-Destination (OD) matrices in quantities and deliveries per transport service type, time slice and vehicle type. The latter allows us to obtain the vehicle OD matrices according to the journey characteristics (i.e. number and sequence of delivery points) in order to restock economic activities located within the study area. This approach, known in literature as tour-based approach, aims at reproducing the choice structure of the restocking process and the sequence of delivery points (stops) for vehicle journeys, considering dependences existing among subsequent trips of the same journey. It implies that each destination zone to be delivered is chosen depending on the previous and the next destination ones. The logistic subsystem of the proposed modelling system can be divided in two parts: the first which defines the trip chain order (i.e. the number of deliveries made during a tour); the second one which considers the choice of the stop locations. This paper focuses on the specification and calibration of a trip chain order model using data collected in the city centre of Rome

bus travel time variability some experimental evidences

Abstract Bus travel time analysis is essential for transit operation planning. Then, this topic obtained large attention in transport engineering literature and several methods have been proposed for investigating its variability. Nowadays, the availability of large data quantities through automated monitoring allows more in-depth this phenomenon to be pointed out with new experimental evidence. The paper presents the results of some analyses carried out using automatic vehicle location (AVL) data of bus lines and automated vehicle counter (AVC) data on some corridors in the urban area of Rome where the bus services are mixed with other traffic and travel times are subject to high degrees of variability. The results show the effect of temporal dimension and similarity between travel time and traffic temporal patterns, and could open the road for the improvement of the short-term forecasting methods, too

Investigating bus travel time and predictive models: a time series-based approach

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Urban Freight Transport Demand Modelling: a State of the Art

The paper provides a review of freight transport demand models for applications in urban and metropolitan areas. The perspective adopted is the short-term one of public decision-makers involved in transport planning and traffic management. The paper recalls the general methodology to be used for assessing the city logistics scenario and the features of models in relation to the planning horizons: strategic, tactical and operative. The focus is on the transport demand models able to support the assessment of short-term policies/measures. Several models and methods have been proposed. They usually refer to the multi-stage modelling approach and can be classified in terms of reference unit: truck/vehicle, commodity/quantity, delivery and mixed. The paper offers an analysis of pros and cons of each above classes of models. The research prospects are also identified