Methodology for an integrated modelling of macro and microscopic processes in urban transport demand

The paper presents the theoretical formulation and the underlying assumptions for an activity-based approach of transport demand modelling. Starting with the definition of a time hierarchy of decision-making in the urban environment, rules are formulated that dictate the general hierarchic structure of individuals’ choices in the urban system. The temporal scale defines decisions for activities and their daily sequence, the geographical scale decisions associated to destination choice processes. We build activity plans (number and daily sequence of activities) from an empirical data set and calculate trip paths (time-spatial trajectories including transport modes and travel destinations) assuming consumers to maximize their utility in the decision-making process. First results of the translation of the theoretical model into a real-world application are shown for the city of Santiago, Chile

Discussion note by Prof. Joop Evers: Profile of new intercity transport

The massive use of private cars causes serious problems. Despite parking problems and traffic congestions, for many the private car remains favourable. As an alternative, a new type of public intercity transport has been proposed, called Fast Transport on Request (FTR). Specific elements are: dedicated tracks for high speed bus driving between the cities; booking via Internet or intelligent telephone; intelligent logistics control reducing the number of stops and excluding detours; adaptive seats and digital facilities

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method’s performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively