Kazushi Sano

Commodity Distribution Model Incorporating Spatial Interactions for Urban Freight Movement

The commodity distribution model proposed in this paper is developed in such a way that the movement of commodities is explained as an outcome of their flow through several freight agents in a supply chain. As commodity flow is fundamentally determined by demand, the proposed model was developed from a discrete choice model that considered the individual behavior of a customer to decide the suppliers from which to purchase and the amount of commodity to acquire from each of them. The model not only takes into account the interplay between shipper and customer in a supply chain but also captures the spatial interactions among alternatives and among customers, because spatial effects generally affect customer preference. In this study, several model specifications were developed and compared, with and without incorporating spatial interactions. The empirical results of the model, which were applied to analyze the urban commodity distribution in the Tokyo metropolitan area, indicate that integrating both spa...

Location Choice Model for Logistic Firms with Consideration of Spatial Effects

An overview is presented of a location choice model for logistic firms that takes into account spatial effects. A modeling framework is developed to analyze decisions about location choice for logistic firms by using mixed logit models. In this framework, spatial effects such as the correlation between firms in deterministic terms and the spatial correlation among zones in the error term are captured by mixed logit models. In addition, the dynamics of the consumption of commodities between consumers and suppliers is considered. In a case study, the developed framework is applied to the Tokyo metropolitan area. The model is tested for retailers, wholesalers, and manufacturers. The results of the study indicate that for location choice, the number of employees is a more important determinant for manufacturers than it is for retailers and product wholesalers. Additionally, the results indicate that the spatial effects and the land prices in a given zone strongly affect the decision-making process of all the firms in the metropolitan area.

Estimating Truck Trip Origin-Destination with Commodity-Based and Empty Trip Models

This study estimates origin–destination (O-D) matrices of light and heavy trucks on the basis of the flow of commodities in the Tokyo metropolitan area. The truck O-D matrix is generally determined by either vehicle trip-based or commodity-based approaches, although the former cannot distinguish between loaded and empty trips and does not characterize the shipments. Three major concepts are proposed in this study. First, the truck trip O-D is estimated on the basis of the commodity approach because it can utilize the characteristics of the shipments. Second, the main contribution of the model is its ability to estimate both loaded and empty trips by modeling the truck movements as round trips and trip chains. The O-D of truck movement, particularly the movement of loaded trips in round trips or zero-order trip chains, is similar to that of the commodity flows. However, both movements have relatively different O-D when the loaded trips travel from one origin to many destinations or are part of an nth-order trip chain. Finally, the trip chain is modeled on the basis of characteristics such as average payload, adjacent zones, and the commodity O-D providing the most attractive zones traveled by trucks. The performance of the model is demonstrated by using the mean square error between the estimated and observed truck O-D matrices. The model concept is then applied to lightweight products obtained from the food industry. The proposed concept enhances the trip chain behavior and provides better results than the model without trip chain behavior.

Resource Leveling in PERT by Neural Network

Recently, the application of neural network theory are tried in various fields, and are successful. In the field of civil engineering, Akamatsu et al.[1] and Nakatuji [2] showed that the possibility of the application of the theory, for example.

Comparison of the coefficients of RP and SP models for modal choice (a case study of Karachi City, Pakistan)

This study attempted to find out the comparison between the coefficients of the similar exogenous variables of the two multinomial logit models. These models were developed for the city of Karachi, the business capital of Pakistan. The two models were developed with the Revealed Preference and Stated Preference data. The choice set for the model developed with PR data was car, motorcycle, Para transit, bus and coach. All the information about these modes was directly asked from the commuters in the survey carried out for modeling the travel behavior. However in the SP data model, the new mode of magnetic train was included in the choice set and the surveyors gave all the information to the commuters and they were asked about their preference. The coefficients of variables used in the utility functions were calibrated by using the HIELOW software. Chi-Square analysis was carried out to find out the difference between the coefficients of the similar exogenous variables of the two models. It was revealed that the difference between the coefficients of travel time public, car ownership, motorcycle ownership, income coach, age bus and age Para transit and household public for RP and SP data was found to be significant at 95 % confidence level. It was found that mostly variables of the RP data set related with public transportation were significantly different from the same variables of the SP data set, except car ownership and sex Para transit. As the magnetic train was included in the public mode and as it was observed that the major share from the (Bus and Coach users) will shift to the new magnetic train so the parameters related to public transportation were found to be significant. The reason for this unexpected result of car ownership was that it was related with the modal constant of car, which was comparatively high in the SP data set. For the covering abstract see ITRD E129315.

POLICY SENSITIVITY ANALYSIS OF KARACHI COMMUTERS

An incremental logit model was used in order to measure the change in behavior of commuters after the introduction of the newly proposed transport mode of magnetic train. Car, motorcycle, paratransit, bus, and coach were existing transport modes, while magnetic train was considered to be a future mode. Both direct and cross elasticity were calculated for existing and future conditions of Karachi. For RP data models, it was revealed that total travel time variable had higher direct elasticity of -1.017 for bus, while total cost divided by family income variable had higher effect on the cross elasticity of car, motorcycle, and paratransit. Additional findings are also given and discussed.

Microsimulation for Commuters' Mode and Discretionary Activities by Using Neural Networks

This research aims to develop an activity-based travel demand model and its microsimulation model for commuters’ work-tour mode and their discretionary activities & travel by using neural networks. The model system is designed as a series of hierarchical submodels. At the highest level is a given condition of primary travel pattern of employees between home and workplace, which constrains their behavior whether and how to make discretionary tours. At lower levels of the system are the choice of discretionary travel generation and subsequently the choice of their destination, travel mode and activity duration time. The study employed the person-trip survey data for the metropolitan area of Nagaoka, Niigata conducted by the national government in November 1999. The empirical estimation by neural networks revealed that an activity & tour were not independent but closely interrelated among a daily activity pattern. Next it simulated an individual discretionary travel pattern under a number of conditions assuming the introduction TDM measures such as flexible work times or staggered work hours. The microsimulation showed its practical capability to predict the impacts of TDM measures on daily travel patterns.