Practical Approach to Model Trip Chaining

1998 ◽  
Vol 1645 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Yoram Shiftan
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Academic Research ◽  
Household Survey ◽  
Model System ◽  
Destination Choice ◽  
Type Model ◽  
Trip Chaining ◽  
Model Components ◽  
At Home

The need to model trip chaining has been discussed widely in the travel demand literature, but new approaches generally have been limited to academic research. Trip chaining was modeled in an actual urban area model. The model was developed for Boise, Idaho, on the basis of a household survey of 1,600 households. For this model, a tour was defined as a sequence of trip segments that start at home and end at home. The model distinguishes between two main types of tours: A tour that includes one or more work destinations is defined as a work-related tour (WRT); all other tours are defined as nonwork-related tours (NWRT). A model system was developed by assuming the hierarchy of the model components. The highest-level model estimates auto ownership for the household. On the basis of auto ownership, the frequency of WRT is estimated, and on the basis of the frequency of WRT, the frequency of NWRT is modeled. These three model components produce the number of WRT and NWRT for each household. All subsequent models are estimated at the tour level. The WRT model system includes work destination choice model, tour type model including the number of stops on the way to and from work and midday trips, and secondary destination choice model for all nonwork destinations. The NWRT model was developed in the same way with some structural differences.

Coordinated Daily Activity Pattern Type in an Applied Activity-Based Travel Model System: Specifics of Modeling Large Households and Modality Preferences

2018 ◽  
Vol 2672 (47) ◽  
pp. 57-67
Author(s):  
Gaurav Vyas ◽  
Peter Vovsha ◽  
Danny Givon ◽  
Yehoshua Birotker
Keyword(s):  
Daily Activity ◽  
Choice Model ◽  
Model System ◽  
Current Approach ◽  
Orthodox Jewish ◽  
Working At Home ◽  
Household Level ◽  
Levels Of Detail ◽  
Binary Choice Model ◽  
At Home

This paper describes a modified approach to modeling an individual daily activity-travel pattern (DAP) coordinated at the household level. The model was primarily introduced to handle large households that are typical for the city of Jerusalem. However, the developed method proved useful in adding more behavioral aspects to the model. The study introduced daily modality and added the emphasis on modeling it for all household members simultaneously. It is of special practical value for Jerusalem since such population sectors as ultra-Orthodox Jewish and Arabs are characterized by a large share of persons who have an entire day of travel implemented in a non-motorized fashion. In addition to daily modality, this paper presents a simple but useful approach to understand at-home time-use. A binary choice model was formulated for the main reason of being inactive (working-at-home or other). The paper also discusses possible extensions of the current approach as well as alternative approaches to frame DAP type choice in the context of a complete activity-based travel model (ABM). In particular, this model can be easily extended in order to better address the joint nature of the choices using the Gibbs sampler. The paper discusses how DAP type choice can be framed in many ways and with different levels of detail subject to the ultimate model system design.

scholarly journals Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chang-jun Cai ◽  
En-jian Yao ◽  
Sha-sha Liu ◽  
Yong-sheng Zhang ◽  
Jun Liu
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Travel Demand ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Destination Choice ◽  
Rail Transit ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that theρ2values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.

Systematic Investigation of Variability due to Random Simulation Error in an Activity-Based Microsimulation Forecasting Model

2003 ◽  
Vol 1831 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Joe Castiglione ◽  
Joel Freedman ◽  
Mark Bradley
Keyword(s):  
Travel Demand ◽  
Mode Choice ◽  
Demand Forecasting ◽  
Time Of Day ◽  
Model System ◽  
Destination Choice ◽  
Random Numbers ◽  
Microsimulation Model ◽  
Random Simulation ◽  
Simulation Error

A key difference between stochastic microsimulation models and more traditional forms of travel demand forecasting models is that micro-simulation-based forecasts change each time the sequence of random numbers used to simulate choices is varied. To address practitioners’ concerns about this variation, a common approach is to run the microsimulation model several times and average the results. The question then becomes: What is the minimum number of runs required to reach a true average state for a given set of model results? This issue was investigated by means of a systematic experiment with the San Francisco model, a microsimulation model system used in actual planning applications since 2000. The system contains models of vehicle availability, day pattern choice, tour time-of-day choice, destination choice, and mode choice. To investigate the variability of the forecasts of this system due to random simulation error, the model system was run 100 times, each time changing only the sequence of random numbers used to simulate individual choices from the logit model probabilities. The extent of random variability in the model results is reported as a function of two factors: ( a) the type of model (vehicle availability, tour generation, destination choice, or mode choice); and ( b) the level of geographic detail—transit at the analysis zone level, neighborhood level, or countywide level. For each combination of these factors, it is shown graphically how quickly the mean values of key output variables converge toward a stable value as the number of simulation runs increases.

scholarly journals Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

2021 ◽  
pp. 036119812110288
Author(s):  
Alex van Dulmen ◽  
Martin Fellendorf
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Methodological Approach ◽  
Data Availability ◽  
Data Sources ◽  
Test Case ◽  
Demand Model ◽  
Limited Data ◽  
Demand Models ◽  
Bicycle Infrastructure

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

Do Agritourism Visitors Care about Landscapes? An Examination with Producer-Level Data

2017 ◽  
Vol 57 (3) ◽  
pp. 360-369 ◽  
Author(s):  
Richard T. Melstrom ◽  
Cassandra Murphy
Keyword(s):  
Choice Model ◽  
Destination Choice ◽  
Random Utility ◽  
Utility Model ◽  
Cross Section Data ◽  
Section Data ◽  
Conditional Logit ◽  
Level Data ◽  
Tourist Demand ◽  
Landscape Attributes

This article develops a random utility model of tourist demand for agritourism destinations. Prior research has largely focused on modeling the effect of visitor characteristics and demographics on the demand for agritourism. In contrast, we analyze cross-section data on producer-reported visits to measure the effects of destination attributes. This allows us to examine whether tourists choose destinations based on landscape attributes. The destination choice model is applied to agritourism demand in Oklahoma. We calculate elasticities from both conditional logit and Poisson interpretations of the model. The results provide no evidence that landscapes affect the demand for single-day sites, but do suggest local land use plays a role in the demand for overnight destinations.

scholarly journals AN OIL SPILL MODEL SYSTEM FOR ARCTIC WATERS

1989 ◽  
Vol 1989 (1) ◽  
pp. 517-523
Author(s):  
Malcolm Spaulding ◽  
Tatsusaburo Isaji ◽  
Katherine Jayko
Keyword(s):  
Oil Spill ◽  
Sensitivity Study ◽  
Model System ◽  
Ecosystem Dynamics ◽  
Environmental Data ◽  
Trajectory Data ◽  
Duration Distribution ◽  
Coastal Sea ◽  
Model Components

ABSTRACT An Arctic coastal sea model system, consisting of submodels for weather, hydrodynamics, waves, suspended sediment, ice cover, oil spill trajectory and fates, and ecosystem dynamics is presented. The structure and operation of each sub-model and linkages among the various model components are briefly described. The weather, hydrodynamic, ice, and oil spill fates components of the model system are applied to predict hydrodynamics and oil spill trajectories from selected release points in the Bering Sea. Trajectory data are analyzed to describe the percent of trajectories affecting land and the direction, length, and duration distribution of trajectories. A detailed sensitivity study is performed to determine the role of the various mean current components and wind drift effects in describing spill trajectories. Model predictions highlight the importance of accurately representing the environmental data used as input to the model.

scholarly journals Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies

2021 ◽  
pp. 036119812110444
Author(s):  
Jonathan Stiles ◽  
Armita Kar ◽  
Jinhyung Lee ◽  
Harvey J. Miller
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Travel Demand ◽  
Multinomial Logistic Regression ◽  
High Volume ◽  
Time Of Day ◽  
Bayesian Hierarchical ◽  
Crash Data ◽  
The Road ◽  
At Home

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

scholarly journals Prediction of Commuter’s Daily Time Allocation

2013 ◽  
Vol 25 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Fang Zong ◽  
Jia Hongfei ◽  
Pan Xiang ◽  
Wu Yang
Keyword(s):  
Support Vector Regression ◽  
Travel Time ◽  
Time Allocation ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Model System ◽  
Support Vector ◽  
Activity Duration ◽  
Wide Range ◽  
Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

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