The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents(largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network(WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1(in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.
The effects of socio-demographics, land use characteristics and trip attributes on the commute mode choice are studied with a nested logit （NL） model. Based on the random utility maximum theory, the NL model is formulated. The analysis is carried out in the main area of Nanjing. Direct and cross elasticities are calculated to analyze the effects of travel time and travel cost on the selection of travel mode choice. The results reveal that the non-motorized travel modes are more attractive in the areas with higher housing and employment accessibility and car owners are found to be more likely to commute to work by car. The bus and subway choice probabilities are more sensitive to changes in travel times than to changes in travel costs. In conclusion, a comprehensive public transit system and effective integration of land use and transportation policies help to relieve the traffic congestion levels caused by the increasing urban sprawl.
