Analyzing Cell Phone Location Data for Urban Travel

2015 ◽  
Vol 2526 (1) ◽  
pp. 126-135 ◽  
Author(s):  
Serdar Çolak ◽  
Lauren P. Alexander ◽  
Bernardo G. Alvim ◽  
Shomik R. Mehndiratta ◽  
Marta C. González
Keyword(s):  
Travel Behavior ◽  
Rio De Janeiro ◽  
Cell Phone ◽  
Travel Demand ◽  
Census Data ◽  
Low Cost ◽  
Time Of Day ◽  
Raw Data ◽  
Home Based ◽  
Demand Models

Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin–destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin–destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.

scholarly journals From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior

2017 ◽  
Vol 11 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Rolf Moeckel ◽  
Leta Huntsinger ◽  
Rick Donnelly
Keyword(s):  
Travel Behavior ◽  
Processing Time ◽  
Travel Demand ◽  
Computer Processing ◽  
Professional Judgment ◽  
Trip Generation ◽  
Household Type ◽  
Demand Models ◽  
Generation Mode ◽  
Definition Of

Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

Travel Demand Modeling and Conformity Determination: Atlanta Regional Commission Case Study

2002 ◽  
Vol 1817 (1) ◽  
pp. 172-176 ◽  
Author(s):  
Guy Rousseau ◽  
Tracy Clymer
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Mode Choice ◽  
Time Of Day ◽  
Traffic Model ◽  
Demand Model ◽  
Primary Input ◽  
Land Use Model ◽  
Home Based ◽  
Travel Demand Model

The Atlanta Regional Commission (ARC) regional travel demand model is described as it relates to its link-based emissions postprocessor. In addition to conformity determination, an overview of other elements is given. The transit networks include the walk and highway access links. Trip generation addresses trip production, trip attraction, reconciliation of productions and attractions, and special adjustments made for Hartsfield Atlanta International Airport. Trip distribution includes the application of the composite impedance variable. In the mode choice model, home-based work uses a logit function, whereas nonwork uses information from the home-based work to estimate modal shares. Traffic assignment includes preparation of time-of-day assignments. The model assigns single-occupancy vehicles, high-occupancy vehicles, and trucks by using separate trip tables. The procedures can accept or prohibit each of the three types of vehicles from each highway lane. Feedback between the land use model and the traffic model is accounted for via composite impedances generated by the traffic model and is a primary input to the land use model DRAM/EMPAL. The land use model is based on census tract geography, whereas the travel demand model is based on traffic analysis zones that are subareas within census tracts. The ARC model has extended the state of the practice by using the log sum variable from mode choice as the impedance measure rather than the standard highway time. This change means that the model is sensitive not only to highway travel time but also to highway and transit costs.

scholarly journals Modelos para estimativa da demanda de viagens de veículos de carga para supermercados e shopping-centers

TRANSPORTES ◽  
2010 ◽  
Vol 18 (1) ◽  
Author(s):  
André Gasparini ◽  
Vânia Barcellos Gouvêa Campos ◽  
Márcio De Almeida D`agosto
Keyword(s):  
Literature Review ◽  
Rio De Janeiro ◽  
Travel Demand ◽  
Urban Traffic ◽  
Shopping Centers ◽  
On Demand ◽  
Operational Characteristics ◽  
Demand Models ◽  
Janeiro City ◽  
Freight Vehicles

<p><strong>Resumo:</strong> Neste trabalho apresenta-se uma proposta de modelagem da demanda de viagens de veículos de carga atraída para supermercados e <em>shopping centers</em>. Foram desenvolvidas equações que permitem estimar tal demanda a partir de variáveis relacionadas com as características operacionais e físicas destes empreendimentos. Pretende-se auxiliar na análise dos impactos na circulação urbana com a implantação destes pólos geradores de viagens. Para tanto, foi realizada, inicialmente, uma revisão bibliográfica sobre a demanda para estes tipos de empreendimentos que serviu de base para a escolha das variáveis que vieram a compor o questionário aplicado em 21 supermercados e 7 <em>shopping centers </em>da cidade do Rio de Janeiro. Com base nesta pesquisa obteve-se um perfil da movimentação dos veículos de carga para estes empreendimentos e equações de demanda de viagens atraídas para aos mesmos.</p><p><strong>Abstract:</strong> In this work are proposed models of the travel demand of freight vehicles attracted to supermarkets and shopping centers. Equations were developed for estimating the demand of these trips related to physical and operational characteristics of these enterprises. It intends to aid in the analysis of the impacts on urban traffic with the construction of new supermarkets and shopping centers. Thus, initially it was developed a literature review on demand models for these types of enterprises to choosing the variables to compose the questionnaire which was applied in 21 supermarkets and 7 shopping centers in Rio de Janeiro city. From this research it was obtained the data and characteristics of the movement of freight vehicles for these enterprises.</p>

Case Study in the Application of Adjusted Census Data for Planning Projects

1998 ◽  
Vol 1617 (1) ◽  
pp. 56-63
Author(s):  
Victor J. Siaurusaitis ◽  
Larry J. Saben
Keyword(s):  
Travel Demand ◽  
Census Data ◽  
Model Development ◽  
Traffic Model ◽  
Data Collection Process ◽  
Demand Models ◽  
Home Interview ◽  
Adjustment Factors ◽  
Data Definition ◽  
Census Adjustment

The reasons for differences between locally collected data and the 1990 census data, as determined from a detailed analysis of model development efforts in a planning study, are detailed. Agencies around the country are beginning to use census data that have been adjusted based on newly released Federal Highway Administration publications. The recently completed Transportation Planner’s Handbook on Conversion Factors for the Use of Census Data has been published to assist planners in using the 1990 census to develop and calibrate local travel demand models. Collecting new data to complete the development of a local model is not always an option. The 1990 census provides another source of information to assist in traffic model estimation. Potential users of the census need to be aware that there would appear to be a variance between results obtained from the census journey-to-work files and locally developed home interview surveys, even after the use of the census adjustment factors. The project in Atlanta, Georgia, involved detailed traffic model development and calibration, in conjunction with factor-adjusted census data. Because of the intimate understanding of the data for the study area, and the development of the model set from the beginning, differences between the locally collected data and census were explainable. Possible problems that can arise when comparing the data as they relate to geography, data definition, and accuracy of the data collection process are detailed.

scholarly journals Analysis of Time of Day Fare Discounts on Urban Mass Transit Travel Behavior, Crowding, and Waiting Time

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xiao Guo ◽  
Huijun Sun
Keyword(s):  
Objective Function ◽  
Waiting Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mass Transit ◽  
Time Of Day ◽  
Pressure Equalization ◽  
Schedule Delay ◽  
Urban Mass Transit ◽  
Artificial Network

Every morning, commuters select the regularly dispatched urban mass transit for traveling from a residential area to a workplace. This paper aims to find an optimal discount fare and time intervals on morning peak hour. As a direct and flexible traffic economic instrument, fares can influence commuters’ behavior. Therefore, fare discount has been proposed to regulate traffic flow in different time. Two models have been analyzed to describe it with schedule delay because of the travel demand size. The first objective function is constructed on pressure equalization when the travel demand is small. The other objective function is to minimize total waiting time when the travel demand is large. In the end, numerical examples based on an artificial network are performed to characterize fare discount models.

scholarly journals Departure Time Choice (DTC) Behavior for Intercity Travel during a Long-Holiday in Bangkok, Thailand

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Bhawat Chaichannawatik ◽  
Kunnawee Kanitpong ◽  
Thirayoot Limanond
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Multinomial Logit Model ◽  
Time Of Day ◽  
Travel Patterns ◽  
Peak Period ◽  
Intercity Travel ◽  
Departure Time ◽  
Departure Time Choice

Time-of-day (TOD) or departure time choice (DTC) has become an interesting issue over two decades. Many researches have intensely focused on time-of-day or departure time choice study, especially workday departures. However, the travel behavior during long-holiday/intercity travel has received relatively little attention in previous studies. This paper shows the characteristics of long-holiday intercity travel patterns based on 2012 New Year data collected in Thailand with a specific focus on departure time choice of car commuters due to traffic congestion occurring during the beginning of festivals. 590 interview data were analyzed to provide more understanding of general characteristics of DTC behavior for intercity travel at the beginning of a Bangkok long-holiday. Moreover, the Multinomial Logit Model (MNL) was used to find the car-based DTC model. The results showed that travelers tend to travel at the peak period when the parameters of personal and household are not so significant, in contrast to the trip-related characteristics and holiday variables that play important roles in traveler decision on departure time choice. Finally, some policies to distribute travel demand and reduce the repeatable traffic congestion at the beginning of festivals are recommended.

scholarly journals Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area

2021 ◽  
Vol 11 (16) ◽  
pp. 7654
Author(s):  
Younes Delhoum ◽  
Rachid Belaroussi ◽  
Francis Dupin ◽  
Mahdi Zargayouna
Keyword(s):  
Choice Model ◽  
Time Of Day ◽  
Suburban Area ◽  
Population Synthesis ◽  
Activity Time ◽  
Or Education ◽  
Time To Build ◽  
Home Based ◽  
Demand Models ◽  
Activity Choices

In their daily activity planning, travelers always considers time and space constraints such as working or education hours and distances to facilities that can restrict the location and time-of-day choices of other activities. In the field of population synthesis, current demand models lack dynamic consistency and often fail to capture the angle of activity choices at different times of the day. This article presents a method for synthetic population generation with a focus on activity-time choice. Activity-time choice consists mainly in the activity’s starting time and its duration, and we consider daily planning with some mandatory home-based activity: the chain of other subsequent activities a traveler can participate in depends on their possible end-time and duration as well as the travel distance from one another and opening hours of commodities. We are interested in a suburban area with sparse data available on population, where a discrete choice model based on utilities cannot be implemented due to the lack of microeconomic data. Our method applies activity-hours distributions extracted from the public census, with a limited corpus, to draw the time of a potential next activity based on the end-time of the previous one, predicted travel times, and the successor activities the agent wants to participate in during the day. We show that our method is able to construct plannings for 126k agents over five municipalities, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, which fit adequately real-world time distributions.

scholarly journals Using Probability Distributions for Projecting Changes in Travel Behavior

2021 ◽  
Vol 13 (18) ◽  
pp. 10101
Author(s):  
Andreas Radke ◽  
Matthias Heinrichs
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Probability Distributions ◽  
Human Life ◽  
Decision Makers ◽  
The Past ◽  
Demand Models ◽  
Activity Choices ◽  
Aid Decision ◽  
Pattern Simulation

Mobility is a must for human life on this planet, because important activities like working or shopping cannot be done from home for everyone. Present modes of transports contributes significantly to green house gas emissions while the efforts to reduce these emissions can be improved in many countries. Pathways to a more sustainable form of mobility can be modelled using travel demand models to aid decision makers. However, to project human behavior into the future one should analyze the changes in the past to understand the drivers in mobility change. Mobility surveys provide sets of activity diaries, which show changes in travel behavior over time. Those activity diaries are one of the inputs in activity-based demand generation models like travel activity pattern simulation (TAPAS). This paper shows a method of using probability distributions between person and diary groups. It offers an opportunity for an increased heterogeneity in travel behavior without sacrificing too much accuracy. Additionally it will present the use case of temporal back- and forecasting of changes in activity choices of existing mobility survey data. The results show the possibilities within this approach together with its limits and pitfalls.

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