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Our research question is the usefulness of a high level of spatial granularity for the travel demand when planning a transit line. We formulate a new optimization model for the technology selection and design of a transit line where the spatial attributes of the travel demand can be finely set. The solution method relies on approximated formulae, and we establish relationships with a classic result for the optimal stop spacing. We also present a refinement of the in-vehicle passenger crowding for an existing transit design model where demand spatial attributes are set synthetically. We call ``spatially disaggregate'' and ``spatially aggregate'' the former and the latter model, respectively. These two models are compared by numerical experiments on a scenario for three semi-rapid transit technologies where two variants consider opposite demand profiles in terms of spatial distribution. We conclude that the spatially aggregated model is sufficient when the main goal is technology selection, whereas the spatially disaggregate model is best for design and benchmarking purposes.

Every commuter utilizing urban rail transit decides the departure time from home to a station according to individual judgment for the biggest possibility to board a train as soon as possible after the arrival. Therefore, the departure time choice behavior of the commuters is complicated especially when the transport capacity of this transit line cannot meet the travel demands of its users in rush hour. This research first develops a travel cost function mainly considering the travel time to rationally describe the volume distribution of the commuters arriving at a station at different time periods. Furthermore, an optimization model is accordingly proposed to rationalize the arrival distribution of the commuters on the basis of the amount of the arrived commuters who are able to board a train from the perspective of the systematic operation of a rail transit line for the minimal total travel cost of all the commuters along this line. It is found that the total travel cost can be reduced by 19.0% at most through the arrival distribution rationalization with the optimization model.

This paper proposes a topological analysis of urban transit system based on a functional representation network constructed from the urban transit system in Beijing. The representation gives a functional view on nodes named a transit line. Statistical measures are computed and introduced in complex network analysis. It shows that the urban transit system forms small-world networks and exhibits properties different from random networks and regular networks. Furthermore, the topological properties of the transit-line network are investigated to get some useful conclusions for public transportation engineering.

The implementation of new transit lines is some times dogged by concerns that such lines may increase crime rates in station neighborhoods. Affluent communities have often complained that transit lines transport crime to the suburbs. This study focuses on the Green Line transit system in Los Angeles and examines its effects on crime in the adjacent areas. The Green Line light rail system passes through some high-crime inner city neighborhoods and terminates at its western end in affluent suburban communities. The study examines neighborhood level and municipality-wide crime trends for five years before and five years after the inception of the line. A piecewise regression model is developed to evaluate the impact of the opening of the line in the station neighborhoods. GIS analysis is also utilized to identify spatial shifts in crime hot spots for municipalities abutting the Green Line. At the end, the study establishes that the transit line has not had significant impacts on crime trends or crime dislocation in the station neighborhoods, and has not transported crime from the inner city to the suburbs.