Process Mining for Improving Urban Mobility in Smart Cities: Challenges and Application with Open Data

Urban mobility presents various challenges to favor urban development. These challenges have been traditionally analyzed using transport network optimization and simulation techniques. Nevertheless, it is possible to think of process mining as a complementary approach allowing, among other things, to discover behavioral transportation models, obtain execution measures and detect bottlenecks. The objective of this article is to analyze how suitable PM is for the analysis of urban mobility problems. We use open data from the Metropolitan Transportation System (STM) of Montevideo, Uruguay, which, among other things, provides the ability to record up-to-date information on its transportation network and trips of its citizens. We apply process mining to process discovery, both from buses and their users, and carry out various analyses linking such data with time information, costs, types of users, and city areas

Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data

Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time. This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research. The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis

Private car O-D flow estimation based on automated vehicle monitoring data: theoretical issues and empirical evidence

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented

Estimating Transit Ridership Patterns through Automated Data Collection Technology: A Case Study in San Luis Obispo, California

Public transportation offers a crucial solution to the travel demand in light of national and global economic, energy, and environmental challenges. If implemented effectively, public transit offers an affordable, convenient, and sustainable transportation mode. Implementation of new technologies for information-harvesting may lead to more effective transit operations. This study examines the potential of automated data collection technologies to analyzing and understand the origin-destination flow patterns, which is essential for transit route planning and stop location placement. This thesis investigates the collection and analysis of data of passengers onboard San Luis Obispo Transit buses in February and March 2017 using Bluetooth (BT) and automatic passenger counter (APC) data. Five BlueMAC detectors were placed on SLO Transit buses to collect Bluetooth data. APC data was obtained from San Luis Obispo Transit. The datasets were used to establish a data processing method to exclude invalid detections, to identify and process origin and destination trips of passengers, and to make conclusions regarding passenger behavior. The filtering methods were applied to the Bluetooth data to extract counts of unique passenger information and to compare the filtered data to the ground-truth APC data. The datasets were also used to study the San Luis Obispo Downtown Farmer’s Market and its impact on transit ridership demand. The investigation revealed that after carefully employing the filters on BT data there were no consistent patterns in differences between unique passenger counts obtained from APC data and the BT data. As a result, one should be careful in employing BT data for transit OD estimation. Not every passenger enables Bluetooth or owns a Bluetooth device, so relying on the possession of Bluetooth-enabled devices may not lead to a random sample, resulting in misleading travel patterns. Based on the APC data, it was revealed that transit ridership is 40% higher during the days during which Higuera Street in Downtown San Luis Obispo is used for Farmer’s Market – a classic example of tactical urbanism. Increase in transit ridership is one of the aspects of tactical urbanism that may be further emphasized. With rapidly-evolving data collection technologies, transit data collection methods could expand beyond the traditional onboard survey. The lessons learned from this study could be expanded to provide a robust and detailed data source for transit operations and planning

TRANSIT: Fine-Grained Human Mobility Trajectory Inference at Scale with Mobile Network Signaling Data

International audienceCall detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of proceessing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today\u27s users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs syslog and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort

Urban mobility data analysis in Montevideo, Uruguay

Transportation systems play a major role in modern urban contexts, where citizens are expected to travel in order to engage in social and economic activities. Understanding the interaction between citizens and transportation systems is crucial for policy-makers that aim to improve mobility in a city. Within the novel paradigm of smart cities, modern urban transportation systems incorporate technologies that generate huge volumes of data in real time, which can be processed to extract valuable information about the mobility of citizens. This thesis studies the public transportation system of Montevideo, Uruguay, following an urban data analysis approach. A thorough analysis of the transportation system and its usage is outlined, which combines several sources of urban data. The analyzed data includes the location of each bus of the transportation system as well as every ticket sold using smart cards during 2015, accounting for over 150 GB of raw data. Furthermore, origin-destination matrices, which describe mobility patterns in the city, are generated by processing geolocalized bus ticket sales data. For this purpose, a destination estimation algorithm is implemented following methodologies from the related literature. The computed results are compared to the ndings of a recent mobility survey, where the proposed approach arises as a viable alternative to obtain up-to-date mobility information. Finally, a visualization web application is presented, which allows conveying the aggregated information in an intuitive way to stakeholders

Seamless Mobility: Touchless Commuting

Este projecto visa o desenvolvimento de um sistema de validação sem fios para transportes públicos, que possa substituir o atual conceito de validação com cartões inteligentes (smartcards). Desta forma, a utilização de transportes públicos torna-se mais fluída e simples para os utilizadores e, ao mesmo tempo, existe uma redução dos custos de operação. O sistema está dependente dos telemóveis dos utilizadores para enviar informação contextual relevante, sobre o uso dos transportes públicos. Um dos pontos-chave foi a investigação de tecnologias de comunicação sem fios, como o Bluetooth Low Energy, hardware e ferramentas que pudessem eficientemente determinar o uso de um serviço de transportes por parte dos passageiros. Isto representa uma mudança na abordagem dos transportes públicos - do checkin/checkout ao estar/não estar - que permitiria evitar as limitações da primeira abordagem. O projeto também focou a exploração de diferentes cenários de instalação, tendo em consideração fatores como custo, versatilidade da tecnologia e possibilidade de fraude. O protótipo foi desenvolvido no contexto de um sistema de transportes públicos real, neste caso, a Metro do Porto/STCP. Assim, investigou-se de que forma este sistema funciona, para perceber o que estava implementado, o tipo de investimento que teria de ser feito e qual a relação custo-benefício. A similaridade deste sistema com outros à escala global faz com que os resultados deste projecto possam ser uma referência para futuras implementações de sistemas de validação sem interação.Os testes no terreno permitem concluir que é possível considerar uma solução baseada em Bluetooth Low Energy, desde que a tecnologia seja otimizada, a solução desenvolvida seja aperfeiçoada e os testes continuem, em cooperação com os operadores de transportes públicos.This project envisions the development of a prototype of a wireless "validation system" for public transportation that could replace the current smart card validation concept. This makes the process of using public transports more fluid and simple for the users, while reducing operating costs for the providers. The system relies on the users' smartphones to relay relevant contextual information, regarding the public transport usage. One of the key focus points was the investigation of the existing communication wireless technologies, such as Bluetooth Low Energy, hardware and tools that could efficiently track the passengers' usage of the public transport. This represents an approach shift in public transportation - from checkin/checkout to be-in/be-out - which allows avoiding the former approach limitations.The work also focused on exploring the possible scenarios of deployment, taking in consideration several factors such as cost, versatility of the technology and possibility of fraud.The prototype was developed in the context of an actual public transportation system, in this case, Metro do Porto/STCP. Therefore, research was made on the current system to determine what was already implemented, what type of investment would have to be made and what would be the cost-benefit relationship. The similarity between this system and others around the world, allows the findings of this project to be a reference for future implementations of touchless commuting around the globe.The field test's results make it possible to believe in a solution based on Bluetooth Low Energy, as long as the technology keeps improving, the developed solution is refined and the tests are continued in cooperation with transportation operators