Model And Methodology Of Relationship Between Urban Commute And Residential Workplace Location

There is an even stronger correlationship between urban commuting and jobs-housing spatial distribution during the process of employment suburbanization and residential sprawl. Experience of both domestic and overseas witness the importance of understanding the interaction between transport and activity location for traffic congestion relief and for making practical and plausible transport policy as well. This dissertation aims to present approaches to represent the interaction relationship between urban commuting and residential-employment spatial distribution from microscopic and macroscopic perspective and propose programming models to optimize jobs-housing spatial distribution. Thus, providing some reference for urban transportation planning and urban spatial structure optimization.Firstly, previous studies on the relationship between urban commuting and jobs-housing spatial structure which provide useful theoretical foundation and inspiration for this dissertation are reviewed. Secondly, the interaction mechanism between urban commuting and jobs-housing spatial structure has been explored from both macroscopic and microcosmic perspective. NetworkGEV and WNN are respectively employed to model joint choice of residential workplace location and commute mode split. The predictive and representative capacity of these two models are compared. Thirdly, the impact of the construction of rail transit on residential location and commute mode choice is described by cross-nested loit simultaneous estimation model; meanwhile, LS-SVR is used to forecast real estate prices along urban rail transit in the light of small data samples. Rail transit line 13 in Beijing is taken as examples to illustrate the application of this model. Fourthly, based on jobs-housing balance theory, a multi-optimization model for residential location is established and solved by multi-objective genetic algorithm. Lastly, the relationship between urban commute and jobs-housing spatial structure is evaluated based on Data Envelopment Analysis (DEA) from mesoscopic perspective. A series of evaluating indexes are introduced. From empirical study of 18 Beijing districts, key influence factors of the relationship between urban commute and residence and employment configuration are identified and the non-effective decision making units are improved by projection analysis which is consistent with jobs-housing balance theory.The following are the main innovations of this dissertation: 1. A joint residential location and travel mode choice models under different employment destination scenarios are developed which representing the relationship between jobs-housing spatial distribution and travel to work from a microscopic perspective. Based on random utility maximization theory, discrete choice model specified as Network Generalised Extreme Value(NetworkGEV) has been 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. In addition, a WNN joint choice model is compared with NGEV model in terms of predictability and transferability.2. A LS-SVR model is developed to forecast real estate prices along urban rail transit in the light of the advantage of SVR dealing with small data samples. The kernel function is GaussianRBF providing a higher predictive accuracy than traditional hedonic price approach.3. On account of the advantages of cross-nested logit model structure to allow capturing the potential spatial correlation between spatially contiguous alternatives, cross-nested logit model is specified to estimate the joint choice of residential location and commute mode around the metro stations which help for the understanding the characteristics of spatial traffic distribution. The impact of the scenarios of travel time increasing on the choice probability switching is simulated in this model.4. A multi-objective optimization model is designed to optimize housing units in term of residential location under road network equilibrium. Multi-objective genetic algorithm emerges as the requirement to solve such kind of model. First, multi-objective functions composed of minimum average travel cost from home to workplace, minimum total commute cost and maximum utility of residential location are listed under the environment of road network equilibrium which is considered as constraints for multi-objective optimization model as well. Second, based on the characteristics analysis of multi-objective pareto optimum alternatives, the selection of multi-objective value ordering and combining is proposed to solve this multi-objective optimization model in which jobs-housing balance is remained, thus decreasing the excess commuting and relieving traffic congestion. Finally, a numerical example of residential location optimization is presented and the road network is configured to link the residential zones to workplace zones. This model is simulated by multi-objective genetic algorithm and is provided with a set of pareto optimum alternatives. 5. Data envelopment analysis is explored to establish the impact evaluation model and on the basis of fuzzy mathematics theory, the impact of residential and employment spatial configuration on the commute travel is correspondingly analyzed. Projection analysis is consequently carried out to improve non-effective DMUs and thus optimizing the employment spatial distribution.