Abstract

The story of the taxi in the system of urban mobility is a story of so much unmet potential: There are many opportunities for taxis to play a much larger role in urban mobility, and yet, transportation planners have by and large overlooked these opportunities in practice. In this thesis, I examine whether perceptions--internalized and likely unexamined assumptions about the taxi, largely false--are to blame. I highlight seven commonly held misconceptions about the taxi, and survey a sample of 133 transportation planners to gauge the extent to which these misconceptions are held to be true. The results of the survey at least partially confirm the hypothesis. Taxis appear to be viewed as neither friend nor foe: an isolated industry divorced from transportation concerns, rather than a true partner in a comprehensive system of urban mobility.

Abstract

In many big cities, the bike-sharing system (BSS) and taxi play critical roles in transportation services. They both offer on-demand transportation options and allow flexible riding scheduling and routing. Previous literature has compared BSS and taxi to other transport modes, such as public transit and private automobile, but little is known about the spatiotemporal factors that influence travel choices between these two alternatives. Understanding travel patterns of BSS and taxi is critical in traffic demand analysis and sustainable transportation planning. Also, an in-depth examination of the patterns of travel behaviors, especially when one would choose BSS over a taxi, will provide valuable insights on human mobility and active living research. In this study, we investigated the spatiotemporal patterns of BSS and taxi trips in Chicago from 2014 to 2016. To model travel choices between BSS and taxi, we applied machine learning techniques to simulate the means of transport based on environmental and temporal factors. Results show seasonal trip variations of the BSS and a declining trend of taxi trips. BSS speed is relatively stable while taxi speed varies primarily because of time and locations. Based on the random forest model, which has demonstrated the best fit with high processing speed, travel distance and the number of parks and recreational facilities seem to be critical spatial predicting factors of the travel choice. Given any time and location, the model can recommend the travel choices between BSS and taxis for users. This study shows the significance of machine learning techniques in urban mobility research. Results of the study may potentially support people's transportation decision-making and facilitate sustainable transportation planning.

Abstract

Electrification of mobility exceeds personal transport to increasingly focus on particular segments such as city logistics and taxis. These commercial mobility segments have different motives to purchase a full electric vehicle and require a particular approach to incentivize and facilitate the transition towards electric mobility. A case where a municipality was successful in stimulating the transition to electric mobility is the taxi sector in the city of Amsterdam. Using results from a survey study (n = 300), this paper analyses the differences in characteristics between taxi drivers that either have or do not have interest in purchasing a full electric taxi vehicle. Results show a low intention across the sample to adopt a full electric vehicle and no statistically significant differences in demographics between the two groups. Differences were found between the level of acceptability of the covenant, the rated attractiveness of the incentives, the ratings of full electric vehicle attributes and the consultation of objective and social information sources. These results can be used by policy makers to develop new incentives that target specific topics currently influencing the interest in a full electric taxi vehicle. Document type: Article

Abstract

reasonable taxi fleet size has a significant impact on the satisfaction of urban traffic demand, the alleviation of urban traffic congestion, and the stability of taxi business groups. Most existing studies measure the overall scale by using macro indices, and few studies are from the micro level. To meet the transportation demand for taxis, mitigating the mismatch between taxi supply and demand, this research proposes an urban taxi fleet size calculating model based on GPS tracking data. Firstly, on the basis of road network segmentation, the probability model of a passenger taxi-taking a road section as a unit is built to evaluate the difficulty of taxi-taking on a road section. Furthermore, a user queuing model is built for the &ldquo difficult to take a taxi&rdquo road section in the peak period, and the service mileage required by potential taxi users is calculated. After that, a transportation capacity measurement model is built to estimate the number of taxis required in different time periods, Finally, the income constraint model is used to explain the impact of different vehicle fleet sizes on the income of taxi groups, so as to provide a reference for the determination of the final fleet size. The model is applied to data from Xi&rsquo an. The calculation results are based on data from May 2014, and show that the scale of taxi demand is about 654&ndash 2237, and after considering the impact of different fleet size increases on income, when the income variation index is limited to 0.10, i.e., the decrease of drivers&rsquo income will not exceed 10%, an increase of 1286 taxis will be able to meet 66% of the unmet demand in the peak period. The conclusion indicates that the model can effectively calculate the required fleet size and formulate the constraint solutions. This method provided can be considered as a support for formulating the regulation strategy of an urban taxi fleet size. Document type: Article