scholarly journals Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

2020 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Helai Huang ◽  
Jialing Wu ◽  
Fang Liu ◽  
Yiwei Wang
Keyword(s):  
Waiting Time ◽  
Urban Areas ◽  
Transportation Systems ◽  
Trajectory Data ◽  
Peak Period ◽  
Important Indicator ◽  
Destination Attractiveness ◽  
Urban Accessibility ◽  
Spatio Temporal ◽  
Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

scholarly journals Understanding spatio-temporal mobility patterns for seniors, child/student and adult using smart card data

2014 ◽  
Vol XL-1 ◽  
pp. 167-172 ◽  
Author(s):  
X. Huang ◽  
J. Tan
Keyword(s):  
Urban Areas ◽  
Weighted Graph ◽  
Human Movement ◽  
Travel Behaviour ◽  
Transportation Systems ◽  
Global Network ◽  
Mobility Patterns ◽  
Urban Dynamics ◽  
Spatio Temporal ◽  
Using Data

Commutes in urban areas create interesting travel patterns that are often stored in regional transportation databases. These patterns can vary based on the day of the week, the time of the day, and commuter type. This study proposes methods to detect underlying spatio-temporal variability among three groups of commuters (senior citizens, child/students, and adults) using data mining and spatial analytics. Data from over 36 million individual trip records collected over one week (March 2012) on the Singapore bus and Mass Rapid Transit (MRT) system by the fare collection system were used. Analyses of such data are important for transportation and landuse designers and contribute to a better understanding of urban dynamics. <br><br> Specifically, descriptive statistics, network analysis, and spatial analysis methods are presented. Descriptive variables were proposed such as density and duration to detect temporal features of people. A directed weighted graph G &equiv; (N , L, W) was defined to analyze the global network properties of every pair of the transportation link in the city during an average workday for all three categories. Besides, spatial interpolation and spatial statistic tools were used to transform the discrete network nodes into structured human movement landscape to understand the role of transportation systems in urban areas. The travel behaviour of the three categories follows a certain degree of temporal and spatial universality but also displays unique patterns within their own specialties. Each category is characterized by their different peak hours, commute distances, and specific locations for travel on weekdays.

scholarly journals INVESTIGATING THE POTENTIAL OF ACTIVITY TRACKING APP DATA TO ESTIMATE CYCLE FLOWS IN URBAN AREAS

2016 ◽  
Vol XLI-B2 ◽  
pp. 515-519
Author(s):  
J. Haworth
Keyword(s):  
Temporal Variation ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Temporal Structure ◽  
Transportation Systems ◽  
Spatial And Temporal Variation ◽  
Linear Regression Models ◽  
Activity Tracking ◽  
Spatio Temporal ◽  
On The Road

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p&lt;0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

scholarly journals VISUALIZATION OF SPATIO-TEMPORAL RELATIONS IN MOVEMENT EVENT USING MULTI-VIEW

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1469-1476
Author(s):  
K. Zheng ◽  
D. Gu ◽  
F. Fang ◽  
Y. Wang ◽  
H. Liu ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Multiple Scales ◽  
Temporal Relations ◽  
Discovery Process ◽  
Trajectory Data ◽  
Statistical Tools ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Visual Interface ◽  
Taxi Trajectory

Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

scholarly journals SPATIO-TEMPORAL CHARACTERISTICS OF RESIDENT TRIP BASED ON POI AND OD DATA OF FLOAT CAR IN BEIJING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 99-105
Author(s):  
N. Mou ◽  
J. Li ◽  
L. Zhang ◽  
W. Liu ◽  
Y. Xu
Keyword(s):  
Research Unit ◽  
Concentric Circle ◽  
Relative Dispersion ◽  
Residential Areas ◽  
Trajectory Data ◽  
Periodic Patterns ◽  
Temporal Characteristics ◽  
The Road ◽  
Spatio Temporal ◽  
Taxi Trajectory

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according to the characteristics of the taxi trajectory data distributed along the road network, it takes the Voronoi generated by the road nodes as the research unit. This paper proposes a hybrid clustering method &amp;ndash; based on grid density, which is used to cluster the OD (origin and destination) data of taxi at different times. Then，combining with the POI data of Beijing, this research calculated the density of the POI data in the clustering results, and analyzed the relationship between the activities of residents in different periods and the functional types of the region. The final results showed that the residents were mainly commuting on weekdays. And it found that the distribution of travel density showed a concentric circle of the characteristics, focusing on residential areas and work areas. The results of cluster analysis and POI analysis showed that the residents' travel had experienced the process of "spatial relative dispersion &amp;ndash; spatial aggregation &amp;ndash; spatial relative dispersion" in one day.

scholarly journals El capital de la movilidad urbana cotidiana: Motilidad en la periferia de Lima Metropolitana

2019 ◽  
Vol 22 (1) ◽  
Author(s):  
Gerardo D. Regalado-Regalado
Keyword(s):  
Latin American ◽  
Participant Observation ◽  
Transportation Systems ◽  
Structured Interviews ◽  
Palabras Clave ◽  
Urban Accessibility ◽  
Physical Abilities ◽  
Spatio Temporal ◽  
Space Production ◽  
The City

Resumen Los procesos de movilidad urbana en las ciudades latinoamericanas se enfrentan a un territorio fragmentado, desigual, no inclusivo, que altera y fragmenta las condiciones espacio-temporales sobre las cuales se construye la espacialidad urbana de la vida cotidiana, y por ende afecta sus desplazamientos. Es por lo que se hace necesario estudiar las prácticas sociales de viajes y la relación directa con el capital de motilidad que ostenta un individuo y que le permite ejercer su habitus ambulante. En la zona periférica de Tahuantinsuyo en la ciudad de Lima (Perú), se aplicó un método etnográfico, de tipo micro-etnografía-particularista, combinando observación no participante y entrevistas semi-estructuradas, lo que permitió la recolección de datos cuantitativos y cualitativos para caracterizar la condición de accesibilidad, competencias, agencia y apropiación con el fin de determinar el capital de motilidad. Como resultado se determinó que este capital es alterado en función no solo a la posición socio-económica y la competencia con otros agentes, sino también, en relación a su grado de accesibilidad a su hábitat, sus capacidades físicas, habilidades o competencias, encontrando diversas y coincidentes prácticas sociales de viajes que alteran dicho capital. Palabras clave: accesibilidad urbana; derecho a la ciudad; habitus ambulante; metropolización; producción del espacio; segregación urbana; sistemas de transporte;   AbstractThe processes of urban mobility in Latin American cities face a fragmented, unequal, non-inclusive territory that alters and fragments the spatio-temporal conditions on which the urban spatiality of everyday life is built, and therefore affects its displacements. That is why it is necessary to study the social travel practices and the direct relationship with the capital of motility that an individual has and that allows him to exercise his traveling habitus. In the peripheral area of ​​Tahuantinsuyo in the city of Lima (Peru), an ethnographic method was applied, of the micro-ethnography-particularist type, combining non-participant observation and semi-structured interviews, which allowed the collection of quantitative and qualitative data for characterize the condition of accessibility, competencies, agency and appropriation in order to determine the motility capital. As a result, it was determined that this capital is altered based not only on the socio-economic position and competition with other agents, but also, in relation to their degree of accessibility to their habitat, their physical abilities, skills or competencies, finding various and matching social travel practices that alter said capital. Keywords: urban accessibility; right to the city; walking habitus; metropolization; space production; urban segregation; transportation systems;   Recibido: septiembre 9 / 2019  Evaluado: noviembre 29 / 2019  Aceptado: diciembre 13 / 2019 Publicado en línea: diciembre de 2019                 Actualizado: diciembre de 2019

scholarly journals INVESTIGATING THE POTENTIAL OF ACTIVITY TRACKING APP DATA TO ESTIMATE CYCLE FLOWS IN URBAN AREAS

2016 ◽  
Vol XLI-B2 ◽  
pp. 515-519 ◽  
Author(s):  
J. Haworth
Keyword(s):  
Temporal Variation ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Temporal Structure ◽  
Transportation Systems ◽  
Spatial And Temporal Variation ◽  
Linear Regression Models ◽  
Activity Tracking ◽  
Spatio Temporal ◽  
On The Road

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p&lt;0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

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