Carolina Palma

Bio: Carolina Palma is an academic researcher. The author has contributed to research in topics: Data collection & Automatic vehicle location. The author has an hindex of 3, co-authored 6 publications receiving 400 citations.

Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile

Abstract: A high-quality Origin–Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile The proposed method is applied to two 1-week datasets obtained for different time periods From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions The results are available at any desired time–space disaggregation After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices

Estimation of a Disaggregate Public Transport OD Matrix from Passive SmartCard Data from Santiago, Chile

Abstract: A good quality Origin-Destination(OD) matrix is a fundamental prerequisite for any serious strategic transport system analysis. However, is not always easy to obtain it, as OD matrices are expensive and difficult to obtain. This is particularly relevant in large cities, with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods. Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection. In this paper the authors present a methodology for estimating an OD matrix from smartcard and GPS data for Santiago, Chile. The authors applied the proposed method to a one-week database, obtaining detailed information for time and position of boarding, time and position of alighting for 80% of the 36 million boarding transactions. The results are available at any desired time-space disaggregation. After some post processing, and incorporating expansion factors to account for unobserved trips the authors build OD matrices disaggregated at bus stop level.

Towards a Reliable Origin-Destination Matrix from Massive Amounts of Smart Card and GPS Data: Application to Santiago

Abstract: Purpose — Automated fare collection systems implemented in public transportation systems in the last decade have provided a massive, continuous and low-cost source of reliable travel information. A direct and useful application of these data is the estimation of highly representative, although not bias-free, origin-destination (OD) matrices. Methodology/approach — We discuss several issues with current OD matrix estimation methodologies, such as fare evasion and group travel, and their derived biases, specifically focusing on the Santiago (Chile) case. We also propose and apply two methods of validation: endogenous and exogenous validation. We elaborate on some methodological improvements that could be implemented to upgrade the activity estimation mechanics. Findings — Several sources of bias in the estimation of OD matrix estimation from passive data are pointed and some solutions proposed. We apply improvements to existing methodologies and increase the success rate of trip estimations. Practical implications — The reliable estimation of public transport OD matrices from passive data results in a valuable planning tool for both transit authorities and operators, much more representative and with less errors and biases that conventional data collecting techniques. Originality/value of paper — This paper is one of the first works to deal with the subject.

Indirect Measurement of Level of Service Variables for the Public Transport System of Santiago Using Passive Data

Abstract: Purpose — The introduction of new technology to public transport systems has provided an excellent opportunity for passive data collection. In this paper, we explore the possibility of automatically generating level of service indicators that could be used for operation planning and monitoring of Transantiago, the public transport system of Santiago, Chile. Design/methodology/approach — After basic processing of the raw automatic vehicle location (AVL) and automatic fare collection (AFC) data, we were able to generate bus speed indicators, travel time measurements and waiting time estimates using data from 1week. The results were compared with manual measures when available. Findings — The advantage is that these measurements and estimates are reliable because they are obtained from large samples and at nearly no cost. Moreover, they can be applied to any set of data with a selected periodicity. Research limitations — The scope of this research is limited to what can be observed with AVL and AFC data. Additional information is required to incorporate other dimensions, such as personal characteristics and/or more detail in the origin/destination (OD) of the trips. Practical implications — Nevertheless, these results are valuable for the planning and operation management of public transport systems because they provide large amounts of information that is difficult and expensive to obtain from direct measurements. Originality/value — This paper proposes tools to obtain valuable information at a low cost. These tools can be implemented in many cities that have certain technological devices incorporated into their public transport systems.

Obtención de Información Valiosa a partir de Datos de Transantiago

Abstract: En este articulo se presenta un resumen de la investigacion conjunta que han sostenido la Universidad de Chile con el Ministerio de Transportes y Telecomunicaciones, orientada al desarrollo de metodos sofisticados para procesar informacion y estimar indicadores a partir de las bases de datos de posicionamiento GPS y transacciones bip! de Transantiago. La informacion pura de transacciones permite realizar analisis estadisticos de los perfiles horarios de la demanda y del uso del medio de pago y modos de transporte y servicios. Al cruzar esa informacion con la de posicionamiento de buses, es posible tambien incorporar la dimension espacial al analisis. Se describe las bases de datos con las cuales se trabaja, y se muestra para distintos cortes temporales dos aplicaciones de la metodologia en desarrollo; la primera es la estimacion de velocidad comercial del sistema de transporte publico, donde los analisis consideran casos de desagregacion tanto espacial como temporal, obteniendo un muy buen diagnostico del rendimiento del sistema de transporte publico en este sentido; la segunda es el procedimiento de estimacion de paradero de bajada, del cual se logra obtener perfiles de carga de buses y servicios, ademas de matrices origen destino de viajes en transporte publico, que pueden ser desagregados espacial y temporalmente. Estos resultados son solo una muestra del enorme potencial que tiene este procesamiento de datos.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Abstract: Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Mining smart card data for transit riders’ travel patterns

Abstract: To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the “magnitude” level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders’ historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.

Detecting the dynamics of urban structure through spatial network analysis

Abstract: To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.

Demand-driven timetable design for metro services

Abstract: Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.