Guido Cantelmo

University of Luxembourg · Research Unit in Engineering Science (RUES)

In bike-sharing systems, the inventory level is defined as the daily number of bicycles required to optimally meet the demand. Estimating these values is a major challenge for bike-sharing operators, as biased inventory levels lead to a reduced quality of service at best and a loss of customers and system failure at worst. This paper focuses on usi...

Traffic flow/volume data are commonly used to calibrate and validate traffic simulation models. However, these data are generally obtained from stationary sensors (e.g., loop detectors), which are expensive to install and maintain and cover a small number of locations in the transport network. On the other hand, Floating Car Data (FCD) are readily...

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better support low-latency applications. In this paper, we tackle the problem of EDC deployment in urban environments. Previous research on mobile phone data has exposed a strong correlation between t...

Carsharing services provide users with a new way of approaching mobility and accessing shared vehicles. Since the initial pilot studies in the early 90s, technological innovations (e.g., advances in mobile technology, increased range of electric cars) and the establishment of new business models (e.g, station–based, free–floating, peer-to-peer, pac...

This paper proposes a data fusion approach to automatically detect activity patterns in a GPS dataset based on travel diaries and correct misclassification errors. The Activity Patterns Detection consists of a Supervised Learning framework, thanks to which the activity purposes in the travel diaries are learned and then predicted in the GPS dataset...

Car-sharing services have been providing short-term car access to their users, contributing to sustainable urban mobility and generating positive societal and often environmental impacts. As car-sharing business models vary, it is important to understand what features drive the attraction and retention of its members in different contexts. For that...

Calibrating DTA models is complex due to the involved indeterminacy, non-linearity, and dimensionality, restricting the application of conventional calibration approaches, especially for larger networks. For this, Principal Component Analysis (PCA) is slowly establishing itself as the new state of the art because it can greatly tackle two well know...

Background The COVID-19 pandemic is a new phenomenon and has affected the population’s lifestyle in many ways, such as panic buying (the so-called “hamster shopping”), adoption of home-office, and decline in retail shopping. For transportation planners and operators, it is interesting to analyze the spatial factors’ role in the demand patterns at a...

Data play an indispensable role in transport modelling. The availability of data from non-conventional sources, such as mobile phones, social media, and public transport smart cards, changes the way we conduct mobility analyses and travel forecasting. Existing studies have demonstrated the multitude and varied applications of these emerging data in...

In this paper, we develop a heuristic model based on Gaussian processes to determine synthetic sets of trips in urban networks, considering only supply‐related information. This is an alternative to the benchmark method used in the literature, which consists of repeating several trials of Monte Carlo simulations and therefore requiring a complex ca...

Dynamic Traffic Assignment (DTA) models represent fundamental tools to forecast traffic flows on road networks, assessing the effects of traffic management and transport policies. As biased models lead to incorrect predictions, which can cause inaccurate evaluations and huge social costs, the calibration of DTA models is an established and active r...

The origin-destination (OD) demand estimation problem is a classical problem in transport planning and management. Traditionally, this problem has been solved using traffic counts, speeds or travel times extracted from location-based sensor data. With the advent of new sensing technologies located on vehicles (GPS) and nomadic devices (mobile and s...

With the increasing availability of big, transport-related datasets, detailed data-driven mobility analysis is becoming possible. Trips with their origins, destinations, and travel times are now collected in publicly available databases, allowing for detailed demand forecasting with methods exploiting big and accurate data. In this paper, we predic...

Retrieving exhaustive information about individual mobility patterns is an essential step in order to implement effective mobility solutions. Despite their popularity, digital travel surveys still require a significant amount of inputs from the respondent. Consequently, they require great efforts from both respondents and analysts, and are limited...

Big, transport-related datasets are nowadays publicly available, which makes data-driven mobility analysis possible. Trips with their origins, destinations and travel times are collected in publicly available big databases, which allows for a deeper and richer understanding of mobility patterns. This paper proposes a low dimensional approach to com...

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data ga...

Time-dependent Origin–Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space...

This paper deals with the problem of estimating daily mobility flows using different sources of data, and in particular from mobile devices, such as mobile phones and floating car data. We show how mobile phone data can be used to better estimate the structure of the demand matrix, both temporally (i.e. the daily generated flows from each zone) and...

Time-dependent Origin-Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space...

This paper deals with the problem of jointly modelling activity scheduling and duration within a Dynamic Traffic Assignment (DTA) problem framework. Although the last decades witnessed an intense effort in developing utility-based departure time choice models, relatively little has been done for understanding how the different assumptions on the ut...

This paper proposes a Dynamic Demand Estimation (DODE) framework that explicitly accounts for activity scheduling and duration. By assuming a Utility-Based departure time choice model, the time-dependent OD flow becomes a function, whose parameters are those of the utility function(s) within the departure time choice model. In this way, the DODE is...

This paper proposes a Dynamic Demand Estimation (DODE) framework that explicitly accounts for activity scheduling and duration. By assuming a Utility-Based departure time choice model, the time-dependent OD flow becomes a function, whose parameters are those of the utility function(s) within the departure time choice model. In this way, the DODE is...

In recent years, multimodal transportation has become a challenging approach to route planning. Most existing planning systems usually rely on data sourced from different organisations, enabling the user to select a limited number of routing strategies. As part of the MAMBA project, developed in Luxembourg until 2017, we have been interested in the...

While estimating origin-destination (OD) demand flows usually requires a large amount of data, nowadays a key issue in traffic engineering is to estimate the trip purpose while protecting user privacy. The aim of this work is to derive from macroscopic and aggregate information the temporal distribution for the production of each traffic zone of a...

This paper deals with the critical problem of solving both the traffic control and assignment problems simultaneously. The main purpose of this work is to assess two well-known local control policies - Equisaturation and P0 - in terms of network-wide performances. While local control policies are, indeed, key building blocks to network-wide approac...

Abstract: In demand estimation problems the number of possible solutions generally increases with the size and the complexity of the network. Hence, it is relevant introducing procedures to reduce the solution space without increasing the problem complexity. Driven by this motivation, this work proposes a procedure to simplify the demand estimation...

This paper analyzes the effects of modeling errors when simulating the real route choice behavior on the solution of travel demand estimation. Firstly, several test networks have been adopted to address such issue, showing the sensitivity of the estimation accuracy to route choice parameters. Then, considering the real network case of Rome (Italy),...

Demand estimation problems based on traffic counts have been investigated for decades. These are traditionally solved by an optimisation problem, where some distance between measured and simulated link flows is minimised, in order to find the most likely origin-destination (OD) flows. To partly limit the effects of solution under-determinedness, ty...

This study focuses on exploiting Call Detail Records (CDR) data in order to detect the demand distribution among different zones within the day, together with information about the type of activity that characterises each zone. Traffic zones are first identified and shaped through a k-means clustering analysis. Then, the traffic between different c...

Traffic control performance on networks depends on the flow response to the policy adopted, which in turn contributes to determine the optimal signal settings. This paper focuses on the relationship between local and network wide traffic control policies within the combined traffic control and assignment problem. Through a full exploration of the s...

In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the c...

In demand estimation problems the number of possible solutions generally increases with the size and the complexity of the network. Hence, it is relevant introducing procedures to reduce the solution space without increasing the problem complexity. Driven by this motivation, this work proposes a procedure to simplify the demand estimation problem i...

In this work, deterministic and stochastic optimization methods are tested Tor solving the dynamic demand estimation problem. All the adopted methods demonstrate difficulty in reproducing the correct traffic regime, especially if the seed matrix is not sufficiently close to the real one. Therefore, a new and intuitive procedure to specify an opport...

This paper presents an in-depth analysis of the bi-level gradient approximation approach for dynamic traffic demand adjustment and the development of new adaptive approaches. Initially, a comparison between the simultaneous perturbation stochastic approximation (SPSA), asymmetric design (AD), polynomial interpolation (PI) method, which was first pr...