Travel Demand Simulation Research Articles

Effects of different mobility concepts in new residential areas

Growing cities need new residential areas, which are often either not connected to the existing transport infrastructure or are poorly connected to it. A fast way to connect these areas is the construction of roads. However, this generates a car-depending mobility among the inhabitants, which is in conflict with several sustainability goals. Moreover, the impact of the implementation of new public transport options is only partly known and this fact reduces the willingness to invest in expensive public transport measures. In this work we examine different mobility concepts, including shared mobility, bicycle highways, a high-frequency bus service, suburban trains and car limitations in a new residential area of 2000 households in Berlin, Germany, which is currently under construction. The households and inhabitants are created synthetically using statistical data derived from a survey among the first people moved in. The age and size structure of these households turn out to be different from the neighboring households. Then, we implement all measures in a microscopic travel demand simulation and quantify the potential modal shifts for four different mobility concepts. The results show that weak and short-term mobility concepts show no significant change in mobility behavior. Only highly integrated projects like bicycle highways into the inner city combined with suburban trains can reduce the need for car-dependent mobility. Shared mobility only fills in the gaps for special occasions but not for daily mobility due to the high costs. In a final step we examine the usage of the introduced public transport services and compare the change in the occupation of the buses and trains. Here our work shows that interchanging from bus to subways and suburban trains drastically reduces the attractiveness of public transport. Introducing a new suburban train changes this situation and the whole region shows a drop of 40% of car trips.

A simulation-based optimization approach for designing transit networks

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Towards agent-based travel demand simulation across all mobility choices – the role of balancing preferences and constraints

This article presents an agent-based travel demand model, where agents react to transport supply across all mobility choices. Long-term choices include mobility tool ownership and work locations. Daily travel patterns are simulated at the individual level by sequentially combining activity frequency, activity durations and destinations as well as a rule-based time-of-day scheduling. A key to success in this novel approach is balancing individual preferences of travelers with system constraints. The model incorporates two types of constraints: 1) capacity constraints of the transport infrastructure. 2) natural time and space constraints during the execution of individual 24-hour day plans. Model results are validated against empirical observations of travel demand in Switzerland. The article concludes with a perspective for further research and development in the field of applied agent-based modeling.

An agent-based evaluation of transit network design

This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorithm that derives new transit routes by inserting neighbouring nodes into existing routes with the aim of improving the demand coverage. The resulting networks are evaluated with an agent-based travel demand simulation model. The use of agent-based modelling is a departure from the existing route expansion literature and indeed the broader transit network design discipline in which the four step model has been extensively used. The procedure is tested on a bus rapid transit network in the City of Cape Town in South Africa.

Incorporating within link dynamics in an agent-based computationally faster and scalable queue model

ABSTRACTThe growing pace of urbanization increases the need of simulation models to handle large-scale scenarios in reasonable time. The present study proposes a fast spatial queue model, which is anchored to an agent-based travel demand simulation framework. The existing queue model is extended to produce more realistic flow dynamics by introducing backward travelling holes to mixed traffic conditions. In this approach, the space freed by a leading vehicle is not immediately available to the following vehicle. The resulting dynamics resembles Newell's simplified kinematic wave model. The space freed corresponding to each leaving vehicle is named as ‘hole’ and, as following vehicles occupy the space freed by leading vehicles, the hole travels backward. This results in triangular fundamental diagrams for traffic flow. The robustness of the model is tested with flow density and average bike passing rate contours. Spatio-temporal trajectories are presented to differentiate the queuing patterns. Finally, a comparison of the computational performance of the different link and traffic dynamics of the queue model is made.

Travelers’ Route Choice: Comparing Relative Importance of Metric, Topological and Geometric Distance

This study investigates the relative importance of ‘metric distance’(MD),‘topological distance’(TD) and ‘geo-metrical distance’(GMD) in determining the route choice behavior of motorized travelers by mode. The study is based on data which has traced the travelers’ actual movements by using mobile GIS applications. MD, TD and GMD were calculated based on space syntax tool within a GIS environment. The findings indicated that GMD is more appropriate than MD in case of travelers’ who use car and motorcycle, whereas MD is more appropriate than GMD travelers’ who use PT and taxi for mode choice modeling, travel demand simulations and automobile navigation systems.

Individuals’ spatial social network choice: model-based analysis of leisure-contact selection

Leisure travel holds an important share of the overall amount of travel. However, efforts in transport planning to model and explain leisure travel have been rather limited for a long time. Only recently, a subcommunity of researchers began to use the methods of social network analysis. Existing research in this area, however, has focused on social interaction behaviour of individuals given their social networks. The selection of friends underlying the generation of social networks has received less attention. To fill this gap, our aims in this study are a descriptive analysis of leisure network data, and specification and estimation of a utility-based friendship-decision model. Data on connected personal leisure networks are obtained by using the personal network approach in an extended survey framework. Results offer insights into the decision of who is in contact with whom and which combinations of attributes have either an increasing or decreasing effect on the utility of a leisure relationship. The model can be used to simulate social network generation in an agent-based travel-demand simulation and with that enable the consideration of relevant behavioural patterns in the simulation of leisure trips.

An agent-based modeling system for travel demand simulation for hurricane evacuation

This paper presents an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate/stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice, and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures pre-evacuation preparation activities using an activity-based approach.A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area for a hypothetical category-4 hurricane is discussed. The simulation results clearly indicate the model system produces a distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 5km of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools.

The effects of new high-occupancy vehicle lanes on travel and emissions

Many urban regions in the U.S. are planning to build extensive networks of new high-occupancy vehicle (HOV) freeway lanes. Past modelling efforts are reviewed and travel demand simulations by the authors are used to demonstrate that new HOV lanes may increase travel (vehicle-miles) and increase emissions when compared to transit alternatives. Recommendations are made for better travel demand modelling methods for such evaluations.