Forecast On Maximum Car Ownership With Constraint Of Environmental Capacity In Urban

Under the bilateral effects of urbanization and mobilization, the major cities have been facing various urban problems such as irregular urban sprawl, house pricing upsurge, traffic demand increase and traffic congestion deterioration. The huge amount of car travel increases the travel quality on one hand, and also consume the limited biofuel resources, resulting in environmental pollution. In this thesis, the road traffic environment load is put into the urban air environment quality indicators, and the urban air environment quality standards are used as the indicators of environmentally sustainability. The aim of this research is to calculate the level of urban car ownership while keeping the air quality and the city state is static within a threshold, as well as the investigation on the upper limit of city allowed car ownership and the development strategies under different urban development forms. This research has various meaningful indications to theoretical and practical aspects.First, a microscopic model is built to predict pollutant concentration of road traffic in order to predict the actual level of concentration of different pollutants. The model which is built using artificial neural network tackles the complex relationship between pollutant concentration and traffic flow, meteorological and urban environment. The unique data collection method for traffic flow is proposed through the technique of virtual circle, which extract accurately the traffic flow on different lanes, reducing the required financial and material resources in conventional survey methods. The method was validated based on the real application and data collection in Dalian city.Second, the thesis builds the static model which predicts the maximum car ownership under the constraint of static environmental capacity. The model which takes the balance between travel demand and traffic pollution into account simulate at the zonal level the sustainable maximum car ownership while the city is static. Bi-level programming technique is adopted to solve the influential relationships between travel demand, environmental pollution and environmental capacity. The upper level problem aims at optimizing the maximum car ownership considering the constraint of environmental capacity, and calculates the zonal car ownership as the decision variable. The lower level problem attempts to minimize the total travel time of the whole network, considering the car ownership calculated from the upper level problem as input, and calculates the optimal travel time and the level of environmental pollution on road links and at zones. The upper and lowers models are inter-connected through a modal split model, which mimic the simulated results of pollution.Lastly, considering the effect of transport policy that would change the carrying capacity of car, a car ownership prediction model of various policy scenarios is proposed. Various policy scenarios are designed and simulated. The consequences of various policy scenarios, effects on mobility and environmental pollution, are further evaluated and analyzed from the aggregated level in cities and the regional level at zones.