Volumen 21 (2020): Heft 4 (December 2020)

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 smartphones), new opportunities have emerged to improve the estimation accuracy and reliability, and more importantly to better capture the dynamics of the daily mobility patterns. In this paper we frame this new data in a comprehensive framework which estimates origin-destination flows in two steps: the first step estimates the total generated demand for each traffic zone, while the second step adjusts the spatial and temporal distribution on the different OD pairs. We show how mobile data can be used to obtain OD matrices that reflect the aggregated movements of individuals in complex and large-scale instances, while speed information from floating car data can be used in the second step. We showcase the added value of big data on a realistic network comprising Luxembourg’s capital city and its surrounding. We simulate traffic by means of a commercial simulation software, PTV-Visum, and leverage real mobile phone data from the largest telco operator in the country and real speed data from a floating car data service provider. Results show how OD estimation improves both in solution reliability and in convergence speed.

This work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

Recent advances in green and smart mobility aim to reduce congestion and foster greener, cheaper and with less delay transportation. The reduction of fuel consumption and CO₂ emissions have worked on light-duty vehicles. However, the reduction of emissions and consumables without sacrificing on emission standards is an important challenge for heavy-duty vehicles. The paper introduces a big data system architecture that provides an on-demand route optimization service reducing NOx emissions of heavy-duty vehicles. The system utilizes the information provided by the navigation systems, big data analytics such as predictive traffic and weather conditions, road topography and road network and information about vehicle payload, vehicle configuration and transport mission to develop a strategy for the best route and the best velocity profile. The system was proven efficient during the performance evaluation phase, since the cumulative engine-out NOx has been decreased more than 10%.

This paper presents the results of a study on the Rome mobility system aiming at estimating the impacts of the progressive lockdown, imposed by the government, due to the Covid-19 pandemic as well as to support decision makers in planning the transport system for the restart towards a post-Covid “new normal”. The analysis of data obtained by the transport monitoring system has been fundamental for both investigating effects of the lockdown and feeding transport models to predict the impacts on future actions. At first, the paper focuses on the so-called transport analytics, by describing mobility trends for the multimodal transportation system of Rome. Then, the results of the simulated scenarios to design public transport services, able to ensure passengers social distancing required in the first post-Covid months, are presented and discussed.

Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.

Smart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

Vehicle-to-Everything (V2X) communication allows infrastructure elements, e.g., traffic lights, to directly communicate with vehicles, thereby allowing services like Green Light Optimized Speed Advisory (GLOSA), Probe Vehicle Data (PVD), and Traffic Signal Priority Request (TSP). The idea behind GLOSA is to assist vehicles approaching an intersection with speed advices in order to fulfill a given objective, e.g., minimizing fuel usage, emissions and/or delay. In a prior work (Kloeppel et al., 2019), several GLOSA algorithms were examined and their fitness (in the form of CO₂ emissions, fuel usage and delay) were evaluated under largely realistic conditions. This paper is an extension of the prior work and presents further examinations, which include a more detailed study on the behaviour of Diesel-powered vehicles when using the GLOSA algorithm of Stebbins et al. (2017) as well as a study considering fixed-time control coupled with intelligent vehicles.