A balance of car ownership under user demand and road network supply conditions

Abstract

Car ownership relates to both user demand and the network supply. When considering this problem, the conventional approach has been mainly concerned with an estimation of car ownership from the user demand viewpoint and has ignored the supply conditions of the road network. Previous related studies have usually modelled car ownership as a function of demography and household characteristics such as income and household size, and have not considered the constraints of the road network. However, road traffic conditions and the availability of parking spaces at home-ends or destination-ends could affect the desire to own a car. The absence of consideration of road network supply constraints in previous modelling of car ownership and usage might be due to the fact that much of these works are of North American and European origins, where space constraints in low density development areas are not important issues for affecting the car ownership and usage. By contrasting with the Hong Kong's road network, it is one of the most densely and busiest road networks worldwide. Thus, the effects of road network supply constraints on car ownership should be fully understood in Hong Kong. In this study, car ownership has been examined from both the demand and supply aspects. On the demand side, an aggregate car ownership model was calibrated by using a set of socio-economic factors. The total number of licensed private cars and motorcycles (in terms of passenger car units) in Hong Kong was estimated in a demand model. A reliability analysis has been devised to incorporate the degree of uncertainty in territory-wide car ownership estimation. By conducting surveys using both revealed and stated preference questions, the effects of the changes of economic factors and fiscal measures on car ownership demand were assessed. Disaggregate car ownership choice (logit-type) models have been calibrated for car ownership and non-car ownership households, respectively. Zonal car ownership households were estimated using the planning data of Hong Kong. On the supply side, the concept of a reserve capacity of car ownership was introduced. Reserve capacity of car ownership is referred to as the greatest additional amount of car ownership that can be accommodated in a traffic zone. A bilevel programming model has been proposed to determine the maximum zonal car ownership that the road network can accommodate, under the existing road capacities and parking space constraints. A heuristic sensitivity analysis based solution algorithm has been derived for solving the reserve capacity problems on car ownership. Artificial road networks were used to test the proposed model and the solution algorithm. A case study in Hong Kong was used to illustrate the application of the proposed model in practice. In the case study, a study road network in Tuen Mun and Yuen Long Corridor of Hong Kong was used for demonstrating the concept of balancing car ownership from user demand and road network supply conditions. With car ownership demand estimated by the disaggregate car ownership choice models and the maximum car ownership determined by the bilevel programming model, a balanced car ownership in the study area has been obtained. The balanced car ownership shows that it is the most efficient scenario in terms of total network travel time and utilization of network facilities. With taking into account of user demand, the balanced car ownership indicates that transport infrastructure improvements should be carried out if the degree of satisfaction of car ownership demand under road network supply conditions is below an acceptance level. This new approach helps the authority estimate car ownership consistently from the view of demand-and-supply. The concept of balancing car ownership has been extended to the problem of balancing parking demand and supply. A bilevel programming model, together with a solution algorithm, has been proposed to investigate the effects of balancing the demand and supply of parking spaces. It was found that balanced parking space optimizes journey time and increases utilization of parking spaces.