Abstract: Relationship between jobs-housing spatial distribution and travel to work were studied from a microscopic perspective. Based on random utility maximization theory, factors depicting the individual and socio-economic characteristics and attribute of travel and land use are defined as exogenous variables, meanwhile, the model choice sets are the combination of residential location choice and commute mode choice subsets. Discrete choice model specified as Network Generalised Extreme Value(NetworkGEV) is employed to investigate the joint decisions of where to live and how to get to workplace which attempts to describe the change of aggravated traffic congestion and discover the potential change caused by residential relocation and travel mode shift under different employment location patterns. This model is estimated in Biogeme, and the direct and cross elasticities are calculated to analyze the change of alternatives probability brought by factors variation. The results reveal that the model yields plausible estimation of exogenous variables in the joint residential location and travel mode choice context. Compared with suburban commuters, commuters in CBD are more sensitive to the increase of travel time and intend to change travel mode and residential location thus trading off the disutility of traffic congestion.

Key words: NetworkGEV model, residential location, commute mode choice, direct elasticities, spatial correlation

摘要： 基于随机效用最大化理论, 选取个人属性、社会经济属性、出行特性和土地利用属性因素为联合选择模型影响变量, 以居住地区位选择集合和通勤出行方式选择集合的组合作为模型的选择项, 构建居住和出行方式联合选择的网络广义极值模型, 刻画日益增长的交通拥堵情况的影响变化及其在不同的就业地模式下对居住再选址和出行方式转变的潜在影响, 从微观角度研究居住就业与城市通勤交通出行关系。利用 Biogeme 软件, 对模型参数进行估计和检验, 同时对模型进行弹性分析, 分析不同影响因素的变化引起的方式选择概率的变化。结果表明, 相比郊区的通勤者, 中心区的通勤者对出行时间的增加更为敏感, 更易于改变出行方式和居住区位, 以抵消交通拥挤引起的负效用。

关键词: 网络广义极值模型, 居住选址, 通勤方式选择, 直接弹性, 空间相关