iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other

Predicting human mobility through the assimilation of social media traces into mobility models

Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure

Assessing the First and Last Mile Problem in Intercity Passenger Rail: Effects on Mode Choice and Trip Frequency

Passenger rail service is an integral part of intercity transportation networks, especially in areas where residents do not have access to cars or other intercity travel options. Some municipalities in the U.S. have experienced a decline in passenger rail service in recent years, which has prompted schedule reductions and entire abandonment of service in some cases. To improve the current intercity passenger rail service predicament, two alternatives can be considered: (1) improve the rail service itself (frequency, infrastructure, etc.) and (2) improve accessibility to the rail stations, which might be cheaper and more cost-effective overall. Improvements in accessibility can impact a wider area and play a key role in passengers choosing rail service as their travel alternative. To address the above issues, the main objective of this thesis was to explore the possibilities for enhancing access to medium distance travel which is, according to the U.S. Bureau of Transportation Services (BTS), between three to five hours or more than 50 miles of travel from home to the nearest intercity passenger rail station. The approach of this thesis was to identify the factors that affect mode choice and level of usage in order to subsequently evaluate different strategies for passengers to reach a station. The Hoosier State Train (HST), a short-distance intercity passenger rail system that travels between Chicago and Indianapolis four days a week, was chosen as a case study. HST has four intermediate stops in Indiana. For some of those intermediate stops, HST is the only intercity public transit service offered to reach either Chicago or Indianapolis. An HST on-board survey that explored opportunities to increase the HST ridership was conducted in November and December of 2016. The survey findings indicated that there are passengers who travel from counties farther away from a county with a station to take the train. Moreover, it was found that most of the respondents drove a personal vehicle, rented a car, or were dropped off to reach a train station in Indiana. The first and last mile (FMLM) of a trip is commonly used to describe passenger travel as far as getting to/from transit stops/stations. The findings of this thesis suggest that there is a gap in the FMLM for intercity rail passengers. Solving the FMLM problem would extend access to transportation systems and could increase the number of passengers from remote communities, such as rural areas. The FMLM problem has been addressed in different public transit contexts, mainly within urban areas; however, limited research efforts have been undertaken to examine the FMLM problem of intercity passenger rail. This thesis intends to fill this gap by exploring the best strategies to address the FMLM problem of short distance intercity passenger rail (i.e., corridors that are less than 750 miles long according to the Passenger Rail Improvement and Investment Act, 2008). Using the data collected on board the HST in Indiana, this thesis estimated a multi-attribute attitude model (MAM) to assess how transportation mode preferences for intercity travel are made and how the factors considered in mode choice decisions vary among individuals with different levels of access to an intercity passenger rail line. An ordered probit model was estimated to further investigate how passenger characteristics, as well as the factors associated with both access to a rail station and mode choice decisions, relate to the frequency of travel by intercity rail. This thesis also presents the results of an accessibility analysis conducted for the state of Indiana in order to identify the areas in need of FMLM service where no public transportation services exist and the cost of reaching a station from a desired origin is expensive. To that end, a cost survey for the different modes available was conducted to determine the average travel cost to the nearest station. The analysis was carried out in ArcGIS using origin-destination information from the on-board survey, transportation network information from the U.S. BTS, and general transit feed specification data. The results of this thesis can assist Amtrak and state transportation agencies identify which aspects of rail service potentially can be enhanced to attract more passengers as well as promote the use of intercity passenger rail service in the U.S. Additionally, the findings could have extensive implications for planning strategies to provide access to passenger rail stations. While the inferences in this thesis are case-study specific for Indiana, the proposed methodology could be used to identify areas where accessibility can be improved in other U.S. states or countries with similar characteristics

Internet of things-based framework for public transportation fleet management in the Free State

Thesis (Masters: Information Technology) -- Central University of Technology, Free State, 2019The poor service delivery by the Free State public transportation system inspired us to design a framework solution to improve the current system. This qualitative study focuses on improving the management of the public transportation fleet. One of the most recently developed technologies in Information and Communication Technology (ICT), namely the Internet of Things (IoT), was utilised to develop this framework. Existing problems were identified through research observations, analyses of the current system, analyses of the current problem areas, as well as participants’ questionnaire answers and recommendations, the participants being the passengers, drivers and vehicle owners. The framework was developed in two phases, namely a hardware phase that makes use of ICT sensors (e.g. RFID, GPS, GPRS, IR, Zigbee, WiFi), and a software phase that uses an internet connection to communicate with the different ICT devices. The software utilised a Graphic User Interface (GUI) to ensure that the software is user-friendly and addresses possible problems and barriers such as multiple language interfaces and different ICT skills levels. The newly designed framework offers different services and solutions to meet the participants’ needs, such as real-time tracking for public transport vehicles to help passengers manage their departure and arrival times, as well as for vehicle owners to monitor their own vehicles. In turn, vehicle arrival notifications will encourage passengers to be on time so that vehicles will not be delayed unnecessarily. Another feature is counting devices that can be installed inside the vehicles, which will inform vehicle owners how many passengers are being transported by a vehicle. The passenger pre-booking system will support the drivers when planning their trips/routes. Finally, the framework was designed to fulfil all the participants’ needs that were indicated in the questionnaires in order to achieve the goal of the research study

Análisis Urbano y Comunidades Inteligentes: Una Aproximación al Empleo de la Tecnología en la Movilidad Cotidiana

Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart communityLa concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201