Network partitioning on time-dependent origin-destination electronic trace data

In this study, we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. These OD pairs consist of electronic traces collected from smartphone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighbourhood level. By means of community detection, we examine the structure of this empirical data set in terms of connectedness. We show that we can distinguish spatially connected regions when we use a performance metric called modularity and the trip directionality is incorporated. From this, we proceed to analyse variations in the partitions that arise due to the non-optimal greedy optimisation method. We use a method known as ensemble learning to combine these variations by means of the overlap in community partitions. Ultimately, the combined partition leads to a more consistent result when evaluated again, compared to the individual partitions. Analysis of the partitions gives insights with respect to connectivity and spatial travel patterns, thereby supporting policy makers in their decisions for future infra structural adjustments

A review of methods to model route choice behavior of bicyclists: inverse reinforcement learning in spatial context and recursive logit

Used for route choice modeling by the transportation research community, recursive logit is a form of inverse reinforcement learning, the field of learning an agent’s objective by observing it’s behavior. By solving a large-scale system of linear equations it allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of IRL models applied to real world travel trajectories and look at some of the challenges with recursive logit for modeling bicycle route choice in the city center area of Amsterdam

Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive l

Door-to-door transit accessibility using Pareto optimal range queries

Public transit is a backbone for well-functioning cities, forming a complicated system of interconnecting lines each with their own frequency. Defining accessibility for public transit is just as complicated, as travel times can change every minute depending on location and departure time. With Pareto optima

Limitations of recursive logit for inverse reinforcement learning of bicycle route choice behavior in Amsterdam

Used for route choice modelling by the transportation research community, recursive logit is a form of inverse reinforcement learning. By solving a large-scale system of linear equations recursive logit allows estimation of an optimal (negative) reward function in a computationally efficient way that performs for large networks and a large number of observations. In this paper we review examples of recursive logit and inverse reinforcement learning models applied to real world GPS travel trajectories and explore some of the challenges in modeling bicycle route choice in the city of Amsterdam using recursive logit as compared to a simple baseline multinomial logit model with environmental variables. We discuss conceptual, computational, numerical and statistical issues that we encountered and conclude with recommendation for further research

Taste variation in environmental features of bicycle routes

In this paper we look at route choice modeling based on observational GPS traces collected by bicyclists in Amsterdam and surroundings. We consider factors influencing bicycle route choice such as distance and environmental factors such as cycle-way infrastructure, land-use environment, tree cover and the effect of noise emitting roads using data from a noise emission model. We estimate a route choice model, comparing multinomial logit, mixed logit and mixed path size logit specifications. Our results show that cyclists have a highly stochastic behavior that are likely to prefer detours to drive over cycle-way infrastructure, near greener landuse and near water, and on less busy roads. Models such as mixed logit that can estimate the stochasticity of cyclists perform best to capture this behavior

The optimization of traffic count locations in multi-modal networks

In this paper we will investigate ways to optimize the placement and number of traffic counters used in multi-modal transportation analysis studies for motorized vehicles, bicycles and pedestrians. The goal is to strike a balance between using as few as possible traffic counters for economical efficiency and deploying more counters which could collect more data. By using shortest path algorithms to determine the paths between the centroids of statistical divisions, we derive from origin-destination matrices which traf

Analyzing potential age cohort effects in car ownership and residential location in the metropolitan region of Amsterdam

Previous research on car ownership has described ownership using a combination of socio-economic factors, demographics, individual preferences and residential factors. However, over the years people's attitudes towards car ownership have changed as new generations are being formed. A new generation of young adults has a different view on car ownership compared to the older generation when car ownership was still a display of status. In this research a first attempt is made to disentangle the effects of age on car ownership and residential location. A discrete choice modelling approach is used where we jointly model car ownership and residential location in the metropolitan region of Amsterdam. We will start from the multinomial logit model and from there try more complex models which capture correlation among alternatives, and introduce a cohort effect for people of a certain age using a nested panel model. The main result of the model shows that car ownership in the city shows more variation in age than car ownership outside of the city

The impact of a new public transport line on parking behavior

To reduce congestion problems in urban environments, policy makers around the world recognize the importance of public transport quality improvement, P&R facilities near peripheral public transport stops, and parking price incentives. This paper proposes a logit model to study the short-term and long-term impact of a new subway line in Amsterdam on the parking behavior. Three groups of travelers are defined in this research: (a) travelers inside Amsterdam, (b) travelers from Amsterdam to outside Amsterdam, and (c) travelers from outside to inside Amsterdam. From the model it is found that in the short term the subway line resulted in an increase in parking near the city center of Amsterdam, especially caused by commuters traveling from outside Amsterdam. However, one year later, the parking demand has dropped significantly which is possibly an effect from increased parking tariffs. Further, before the opening of the public transport line, higher parking tariffs lead to more parking near destination. Experiments with parking tariff cross-variable models reveal that parking tariffs consist of two underlying bi-modal distributions, which are the location of origin and destination with respect to Amsterdam, and whether the time period is during summer or autumn. Parking tariffs affect the parking behavior from and to Amsterdam. Another finding is that during the autumn parking tariffs significantly affect the parking behavior in the short-term. This model can be extended further with more specific location variables, continuing the parking tariffs research, and the addition of more trip, spatial and personal attributes

Using Neural Nets to Predict Transportation Mode Choice: An Amsterdam Case Study

In the Amsterdam metropolitan area, the opening of a new metro line along the north south axis of the city has introduced a significant change in the region’s public transportation network. Mode choice analysis can help in assessment of changes in traveler behavior that occurred after the opening of the new metro line. As it is known that artificial neural nets excel at complex classification problems, this paper aims to investigate an approach where the traveler’s transportation mode is predicted from a choice set through a neural net. Although the approach shows promising results, it has been found that its performance can be attributed partly to the presence of differences in data patterns between the actual and generated trips, which the neural net is able to detect. By adding generated user characteristic attributes, the performance of the model can be boosted slightly overall, and significantly concerning prediction of whether or not a trip was made by car