GS.4C Thursday, July 17 th, 13:30 15:00

Transcription

1 GS.4C Thursday, July 17 th, 13:30 15:00 Presentation Group Room LAEREMANS Michelle Group A Behavior (Traffic Safety and Travel Behavior) C 104 MARCZUK Katarzyna Group B CoopModeling (Agent-based models / ITS / Cooperation in Activity-based Models) C 105 SALANOVA GRAU Josep Maria (13:30 14:15) LIU Feng (14:15 15:00) Group C BigData (Data Fusion Big Data) C 109 GKATZOFLIAS Dimitrios Group D EV (Electric Vehicles) C 110

2 LAEREMANS Michelle UHasselt-IMOB, BE In June 2013, I graduated as a master of science in bioscience engineering with specialization in cell and gene biotechnology. From August 2013 until April 2014, I worked in the clinical research sector. In May 2014, I started working as a PhD student at the university of Hasselt (Transportation Research Institute IMOB) in collaboration with VITO (Flemish Institute for Technological Research). The EU- FP7 PASTA (Physical Activity through Sustainable Transport Approaches; project forms the framework of my PhD and aims at improving health by promoting active mobility as an innovative approach to increase physical activity. The PASTA project consists of a core module and three add-on modules. In the core module, a large longitudinal study will be conducted in which participants are asked to complete a detailed baseline survey on their travel behavior and level of physical activity. Subsequently, they will be asked to regularly fill out shorter follow-up surveys to detect changes in their behavior regarding active mobility. In my PhD project, I will focus on one of the add-on modules on health effects of active mobility. The focus will be on the quantification of the risks, from air pollution exposure, and the benefits, from being physically active. GS.4C Group A (Behaviour: Traffic Safety and Travel Behavior)

3 MARCZUK Katarzyna MIT Aliance for Research and Technology, Singapore I am a second year Singapore-MIT Alliance doctoral student. I am working at Future Urban Mobility Interdisciplinary Group ( on development of the simulation platform called SimMobility. SimMobility is the simulation platform of FM that aims to serve as the nexus of FM research evaluations across the three pillars. It is a parallel & distributed simulation engine to integrate short, medium and long-term modeling capability of urban mobility. Currently my research focus is on automated shared-vehicle mobility-on-demand systems (AMoD). The vehicles are able to drive by themselves and are controlled by a central computer. Autonomous vehicles are designed to operate in a crowded urban environment and take a minimalistic approach to ensure economic feasibility of the system. They are electric (EV). We do plan in Singapore to implement the automated mobility on demand service as a new transportation mode. First we aim to solve the first and last mile issue by providing the service at Mass Rapid Transport (MRT) stations. Customer will be able to request/cancel the request for a vehicle (driver-less taxi) via phone application. The customer will need to specify pickup (and dropoff) locations. The server will receive requests and cancellations from customers, will provide service information to customers such as service status and expected waiting time, will assign vehicles to the customers, will rebalance empty vehicles among different stations (or keep vehicles roaming around the city), will send vehicles for recharging. The server will communicate with each vehicle to obtain its current status and information about network conditions (i.e. travel time) and provide the information about vehicle's next task. My work now is to simulate in SimMobility the AMoD system. I can split my project into five basic cores: 1) Transport network the road network of Singapore is being used, 2) AMoD car model currently I am using a simplified version of human driven models (NGSIM and MITSIM models) 3) Demand generation currently I am using a synthetic data, in the future I plan to integrate the data from the Household Interview Travel Survey (HITS), taxis, contact-less smart cards used for the payment of public transportation fares (EZ-Link) 4) Fleet management this is my main focus. I am developing a vehicle assignment model and route choice model and I plan to combine both to obtain a system optimum model. 5) Metrics for a further analysis how congestion and passengers travel time will be affected by implementing AMoD transportation mode. GS.4C Group B (CoopModeling: Agent-based models / ITS / Cooperation in Activity-based Models)

4 SALANOVA GRAU Josep Maria Certh/Hit, Greece New technologies and social behavior have increased significantly the quantity and quality of mobility-related data available. The current challenge is twofold, on the one side there is a need for developing algorithms able to filter, validate and process vast amounts of data almost in real time, while on the other hand there is a need for developing new applications and services for providing innovative and advanced traveler information services based on theses new data and processing capabilities. I'm working during the last two years in two kinds of probe data sets: one collected by a network of more than 40 static sensors detecting Bluetooth MAC ids and a second one collected by a network of moving sensors (Floating Car Data) from a fleet of more than 1000 taxis. Both networks are installed and running in the city of Thessaloniki and are used for providing both information and routing services to drivers. Additionally, a set of cooperative services for both freight and passengers will be developed during the following 2-3 years, enriching the data sources in terms of quantity and quality. Various algorithms for data filtering, fusion and matching have been developed / are under development for processing, validating and using the collected data, which accounts for millions of daily records. GS.4C (1) Group C (BigData: Data Fusion Big Data)

5 LIU Feng UHasselt-IMOB, Belgium The world s urban population growth and economic development have led to the reshaping of metropolitan space layout among residential, employment and shopping locations, generating growing mismatch between travel demand and transport services. Although a variety of public policies have been introduced to ease the situation of the transport network, the measures are still lagging behind the pace of urban growth. A reliable method to accurately analyze the current mobility demand and underlying transport network systems as well as to identify the areas with serious mismatch problems, is thus important in the assistance of designing more effective measures. With the wide deployment of GPS devices in vehicles in many cities today, we explore the possibility of using GPS data to develop such an approach. Our exploration is composed of four major steps. First, city-wide mobility patterns are modeled based on GPS trajectories generated by vehicles. This model captures a set of key traffic characteristics between each pair of regions of the entire city network, including travel demand, travel speed, and route directness of travel paths. Upon this model, a set of indicators is then built to measure the road transport performance between the regions, and the areas with serious mismatch problems are subsequently pinpointed. Finally, the identified problematic regions are further examined and specific transport problems are analyzed. By applying the proposed method to the Chinese city of Harbin using GPS data collected from all taxis operating in the city between July and September in 2013, the potentials and effectiveness of this technique are demonstrated. With more and more urban vehicles being installed with GPS devices, the designed method can be easily transferrable to the cities, thus paving a way for the development of a new, up-to-date and spatial-temporal sensitive road network analysis approach that supports the urban growth and transport system development into a sustainable future. GS.4C (2) Group C (BigData: Data Fusion Big Data)

6 GKATZOFLIAS Dimitrios Joint Research Centre, Italy I started recently working on an electro-mobility project and I am interested to explore the following relevant topics: Study of driving profiles, parking and charging events for use in infrastructure grid allocation studies. Extraction and transformation of data from electro-mobility modelling tools for further use in other tools. Methodology for optimal geospatial allocation of charging infrastructure. Study current problems and gaps in the collection, monitoring and reporting of electromobility data and projects. GS.4C Group D (EV: Electric Vehicles)