Forecasts of travel demand are often based on data from the most recent time point, even when cro... more Forecasts of travel demand are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. This is because forecasting models with similar contexts have higher transferability, and the context of the most recent time point is believed to be the most similar to the context of a future time point. In this paper, the author proposes a method for improving the forecasting performance of disaggregate travel demand models by utilising not only the most recent dataset but also an older dataset. The author assumes that the parameters are functions of time, which means that future parameter values can be forecast. These forecast parameters are then used for travel demand forecasting. This paper describes a case study of journeys to work mode choice analysis in Nagoya,
Although it is said that disaggregate mode choice models have good transferability, methods of up... more Although it is said that disaggregate mode choice models have good transferability, methods of updating the models have been proposed to improve it. However, temporal transferability of updated models to improve spatial transferability has rarely been analysed and factors affecting it have not been uncovered. This paper has dealt with one of the updating methods, updating alternative-specific constants utilising disaggregate data, and aimed to investigate factors affecting the temporal transferability of the constants. In the analysis, repeated cross-sectional data collected in the Chukyo Metropolitan Area are divided to generate many application areas effectively. The analysis has shown that the factors can relate not only to regional characteristics but also to traffic conditions in the past and irreversibility of travel behaviour.
Trade-off between data newness and number of observations for travel demand forecasting
Transportation Research Board 94th Annual MeetingTransportation Research Board, 2015
Use of recent data is crucial for travel demand forecasting. There is a significant body of evide... more Use of recent data is crucial for travel demand forecasting. There is a significant body of evidence that forecasts by models with recent data outperform those by models with older data. Even when the number of observations from the recent time point is significantly smaller than that from the older time point, utilising the former can result in better forecasts. However, forecasts made by a small number of observations are likely to be problematic where forecast performance is on average worse or is distributed with larger variance. Therefore, there must be a trade-off between data newness and number of observations. An opportunity exists to examine this trade-off in a context of commuting mode choice behaviours by utilising repeated cross-sectional data collected in Nagoya, Japan. Models are estimated utilising different number of observations (ranging from 50 to 10000) obtained from different time points (1971, 1981, and 1991), and they are applied to forecast commuting behaviours of 2001. Bootstrapping is adopted to have insights with statistical meaning. One of results is the following. Compared with models with larger number of observations (in the range of 550–10000 observations) from 1971, models using 1981 data with 300–500 observations and those using 1991 data with 200–250 observations produced statistically significantly better forecasts.
Effects of response formats of stated preference analysis for travel mode switching behaviour - bivariate probit and interval data models for one-and-one-half bound format
Widely used for analysing travel behaviour, SP (Stated Preference) data analysis examines respond... more Widely used for analysing travel behaviour, SP (Stated Preference) data analysis examines respondent preferences under hypothetical conditions. However, no consensus has been achieved concerning the appropriate approaches for SP data analysis and researchers continue to use the process of trial and error. Two of the issues examined in this study are closely related to each other: the response formats of the SP experiment design and the related modelling framework. Regarding the first issue, although the choice format is currently the most frequently used for transport research, attempts have been made to apply the iterative choice format that is commonly used in the CVM (Contingent Valuation Method). One of the iterative choice formats most frequently used, although not for transport analysis, applies a family of double-bounded (DB) formats. Regarding the second issue, the modelling framework must suit the design of the experiment (including the response format), since unsuitable ma...
Travel Demand Forecasts by Using Repeated Cross-Sectional Data: Attempt to Express Parameters as Functions of Gross Domestic Product per Capita
Forecasts by disaggregate travel demand models are often based on data from the most recent time ... more Forecasts by disaggregate travel demand models are often based on data from the most recent time point, even when cross-sectional data are available from multiple time points. However, this is not a good use of data. In his previous work, the author proposed a method for improving the forecasting performance of models by utilising not only the most recent dataset but also an older dataset. He assumes that the parameters are functions of time, which means that parameter values vary over time. He analysed journeys to work mode choice behaviours in Nagoya, Japan. Behaviours in 2001 are forecast using a model with only the most recent 1991 dataset and models that combine the 1971, 1981, and 1991 datasets. His models using data from three time points outperformed the model using only 1991 dataset. This paper extends the author's work by expressing parameters as functions of gross domestic product (GDP) per capita. Although functions of time ascribe all reasons of parameter changes t...
Modelling Car Trip Generation in the Developing World: The Tale of Two Cities
This paper investigates alternative strategies for modelling car trip generation in the context o... more This paper investigates alternative strategies for modelling car trip generation in the context of the developing world. The proposed models particularly aim to capture the potential simultaneity between car trip generation and car ownership decisions, account for potential endogeneity, and deal with possible data limitations in the application context. In this regard, ordered response probit models of car trip generation (with and without the car ownership as an explanatory variable), are compared against combined models where car ownership and car trip decisions are estimated sequentially and jointly. The models are estimated using household survey data from two East African cities, Nairobi, Kenya and Dar-es-Salaam, Tanzania. The model performances are evaluated by comparing the coefficient values, the goodness-of-fit measures and the transferability statistics. Results show that though it is possible to overcome the particular modelling challenges using appropriate model structur...
We investigated household cars and motorcycles ownership behaviours in Asian big cities: Bangkok,... more We investigated household cars and motorcycles ownership behaviours in Asian big cities: Bangkok, Kuala Lumpur, Manila and Nagoya. The behaviours are modelled using bivariate ordered probit models and the impact of accessibility obtained from mode choice models on vehicle ownership is carefully examined. Comparison and temporal and spatial transferability analysis have brought insights into the accessibility measures useful for the modelling.
Disaggregate behaviour choice models have been improved in many aspects, but they are rarely eval... more Disaggregate behaviour choice models have been improved in many aspects, but they are rarely evaluated from the viewpoint of their ability to express intention to change travel behaviour. This study compared various models, including objective and latent models and compensatory and non-compensatory decision-making models. Latent models contain latent factors calculated using the LISREL (linear structural relations) model. Non-compensatory models are based on a lexicographic-semiorder heuristic. This paper proposes 'probability increment' and 'joint probability increment' as indicators for evaluating the ability of these models to express intention to change travel behaviour. The application to commuting travel data in the Chukyo metropolitan area in Japan showed that the appropriate non-compensatory and latent models outperform other models.
Forecasts of travel demand are often based on data from the most recent time point, even when cro... more Forecasts of travel demand are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. This is because forecasting models with similar contexts have higher transferability, and the context of the most recent time point is believed to be the most similar to the context of a future time point. In this paper, the author proposes a method for improving the forecasting performance of disaggregate travel demand models by utilising not only the most recent dataset but also an older dataset. The author assumes that the parameters are functions of time, which means that future parameter values can be forecast. These forecast parameters are then used for travel demand forecasting. This paper describes a case study of journeys to work mode choice analysis in Nagoya,
Although it is said that disaggregate mode choice models have good transferability, methods of up... more Although it is said that disaggregate mode choice models have good transferability, methods of updating the models have been proposed to improve it. However, temporal transferability of updated models to improve spatial transferability has rarely been analysed and factors affecting it have not been uncovered. This paper has dealt with one of the updating methods, updating alternative-specific constants utilising disaggregate data, and aimed to investigate factors affecting the temporal transferability of the constants. In the analysis, repeated cross-sectional data collected in the Chukyo Metropolitan Area are divided to generate many application areas effectively. The analysis has shown that the factors can relate not only to regional characteristics but also to traffic conditions in the past and irreversibility of travel behaviour.
Trade-off between data newness and number of observations for travel demand forecasting
Transportation Research Board 94th Annual MeetingTransportation Research Board, 2015
Use of recent data is crucial for travel demand forecasting. There is a significant body of evide... more Use of recent data is crucial for travel demand forecasting. There is a significant body of evidence that forecasts by models with recent data outperform those by models with older data. Even when the number of observations from the recent time point is significantly smaller than that from the older time point, utilising the former can result in better forecasts. However, forecasts made by a small number of observations are likely to be problematic where forecast performance is on average worse or is distributed with larger variance. Therefore, there must be a trade-off between data newness and number of observations. An opportunity exists to examine this trade-off in a context of commuting mode choice behaviours by utilising repeated cross-sectional data collected in Nagoya, Japan. Models are estimated utilising different number of observations (ranging from 50 to 10000) obtained from different time points (1971, 1981, and 1991), and they are applied to forecast commuting behaviours of 2001. Bootstrapping is adopted to have insights with statistical meaning. One of results is the following. Compared with models with larger number of observations (in the range of 550–10000 observations) from 1971, models using 1981 data with 300–500 observations and those using 1991 data with 200–250 observations produced statistically significantly better forecasts.
Effects of response formats of stated preference analysis for travel mode switching behaviour - bivariate probit and interval data models for one-and-one-half bound format
Widely used for analysing travel behaviour, SP (Stated Preference) data analysis examines respond... more Widely used for analysing travel behaviour, SP (Stated Preference) data analysis examines respondent preferences under hypothetical conditions. However, no consensus has been achieved concerning the appropriate approaches for SP data analysis and researchers continue to use the process of trial and error. Two of the issues examined in this study are closely related to each other: the response formats of the SP experiment design and the related modelling framework. Regarding the first issue, although the choice format is currently the most frequently used for transport research, attempts have been made to apply the iterative choice format that is commonly used in the CVM (Contingent Valuation Method). One of the iterative choice formats most frequently used, although not for transport analysis, applies a family of double-bounded (DB) formats. Regarding the second issue, the modelling framework must suit the design of the experiment (including the response format), since unsuitable ma...
Travel Demand Forecasts by Using Repeated Cross-Sectional Data: Attempt to Express Parameters as Functions of Gross Domestic Product per Capita
Forecasts by disaggregate travel demand models are often based on data from the most recent time ... more Forecasts by disaggregate travel demand models are often based on data from the most recent time point, even when cross-sectional data are available from multiple time points. However, this is not a good use of data. In his previous work, the author proposed a method for improving the forecasting performance of models by utilising not only the most recent dataset but also an older dataset. He assumes that the parameters are functions of time, which means that parameter values vary over time. He analysed journeys to work mode choice behaviours in Nagoya, Japan. Behaviours in 2001 are forecast using a model with only the most recent 1991 dataset and models that combine the 1971, 1981, and 1991 datasets. His models using data from three time points outperformed the model using only 1991 dataset. This paper extends the author's work by expressing parameters as functions of gross domestic product (GDP) per capita. Although functions of time ascribe all reasons of parameter changes t...
Modelling Car Trip Generation in the Developing World: The Tale of Two Cities
This paper investigates alternative strategies for modelling car trip generation in the context o... more This paper investigates alternative strategies for modelling car trip generation in the context of the developing world. The proposed models particularly aim to capture the potential simultaneity between car trip generation and car ownership decisions, account for potential endogeneity, and deal with possible data limitations in the application context. In this regard, ordered response probit models of car trip generation (with and without the car ownership as an explanatory variable), are compared against combined models where car ownership and car trip decisions are estimated sequentially and jointly. The models are estimated using household survey data from two East African cities, Nairobi, Kenya and Dar-es-Salaam, Tanzania. The model performances are evaluated by comparing the coefficient values, the goodness-of-fit measures and the transferability statistics. Results show that though it is possible to overcome the particular modelling challenges using appropriate model structur...
We investigated household cars and motorcycles ownership behaviours in Asian big cities: Bangkok,... more We investigated household cars and motorcycles ownership behaviours in Asian big cities: Bangkok, Kuala Lumpur, Manila and Nagoya. The behaviours are modelled using bivariate ordered probit models and the impact of accessibility obtained from mode choice models on vehicle ownership is carefully examined. Comparison and temporal and spatial transferability analysis have brought insights into the accessibility measures useful for the modelling.
Disaggregate behaviour choice models have been improved in many aspects, but they are rarely eval... more Disaggregate behaviour choice models have been improved in many aspects, but they are rarely evaluated from the viewpoint of their ability to express intention to change travel behaviour. This study compared various models, including objective and latent models and compensatory and non-compensatory decision-making models. Latent models contain latent factors calculated using the LISREL (linear structural relations) model. Non-compensatory models are based on a lexicographic-semiorder heuristic. This paper proposes 'probability increment' and 'joint probability increment' as indicators for evaluating the ability of these models to express intention to change travel behaviour. The application to commuting travel data in the Chukyo metropolitan area in Japan showed that the appropriate non-compensatory and latent models outperform other models.
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Papers by Nobuhiro Sanko