Modeling Week Activity Schedules for Travel Demand Models

2017 ◽  
Vol 2666 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Tim Hilgert ◽  
Michael Heilig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Mean Value ◽  
German Population ◽  
Activity Schedules ◽  
Significance Level ◽  
Annual Survey ◽  
Time Period ◽  
Demand Models ◽  
Kolmogorov Smirnov

Activity schedules are an important input for travel demand models. This paper presents a model to generate activity schedules for one week. The approach, called actiTopp, is based on the concept of utility-based regression models and stepwise modeling. In contrast to most of the existing models, actiTopp covers the time period of one week. Few models have covered one week; thus, the activity generation approach of this simulation period is rare. Analysis of weekly activity behavior shows stability between different days (e.g., working durations). Hence, the model explicitly takes these aspects into account, for example, by defining time budgets to spread durations within the week. For model estimation, the study used data from the German Mobility Panel (MOP). This annual survey collects representative data on the travel behavior of the German population. The data from 2004–2013 provide more than 17,500 activity schedules for one week, with more than 450,000 activities. Selected results are shown for the model application to 2014 MOP data, which the study used for validation purposes. The mean value of activities per person and week show a difference of 0.3 activity. To evaluate the model, the study used Kolmogorov-Smirnov tests with a significance level of α = 0.001. For the activity type distribution of the 2014 sample, the analysis could not reject the null hypothesis of equality of the distribution of the model and the survey data at this significance level.

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.

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.

scholarly journals A joint model of place of residence (POR) and place of work (POW): Making use of Gibbs sampling technique to overcome arbitrary assumptions in contexts of data limitation

2019 ◽  
Vol 12 (1) ◽  
pp. 873-892 ◽  
Author(s):  
Hengyang Zhang ◽  
Jason Hawkins ◽  
Khandker Nurul Habib
Keyword(s):  
Gibbs Sampling ◽  
Travel Behavior ◽  
Ad Hoc ◽  
Travel Demand ◽  
Choice Model ◽  
Sampling Technique ◽  
Demand Models ◽  
Almost All ◽  
High Degree ◽  
Daily Travel

Place or residence (POR) and place of work (POW) are two spatial pivots defining patterns of travel behavior. These choices are considered part of long-term choice influencing short-term daily travel choices. Hence, POR-POW distributions are input into almost all daily travel demand models. However, in many cases, POW-POR is modelled in an ad-hoc way considering the gravity-based or entropy is maximizing aggregate modelling approach. Lack of data on the sequence of choices related to POR and POW is often blamed for avoiding using disaggregate choice model. Recognizing such data limitation, this paper presents an alternative methodology of modelling joint distribution of POW-POW that uses disaggregate choice models without necessarily knowing the sequence of POR and POW choices. It uses the conditional probability break downs of joint POR-POW choice probabilities as depicted in the Gibbs sampling approach. This allows capturing effects of household socioeconomic characteristics, zonal land-use characteristics, and modal accessibility factors in the POR-POW models. The model is applied for a case study in the city of Ottawa. Results reveal that the proposed methodology can replicate observed patterns of POR-POW with a high degree of accuracy.

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.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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