Alexander Erath

Vulnerability Assessment Methodology for Swiss Road Network

This paper presents a methodology that incorporates vulnerability to natural hazards into current infrastructure management systems. The paper is mainly concerned with presenting the methodology applied to assess the transport-related consequences of link failures, including congestion effects on a networkwide scale. Four possible demand shifts caused by single link failures can be expected: detours, shifts in mode choice, shifts in destination choice, and trip–activity suppression. The paper demonstrates that detours are by far the predominant demand reaction. Hence, the quantification of detour-based principal consequences is the main focus. The main challenge was to overcome the calculation time intensity of this equilibrium-based approach. Since demand shift effects were assumed to be spatially restricted around the failed link, subnetworks were used, that is, limited sections of the complete network that were cut out, including their internal and transit demands. The resulting failures turned out to be consistent with those that involved the full network, even for links with long path distances or long detours. On the basis of computed consequences of link failures and on link parameters, a statistical model was developed to reveal and quantify the main factors defining transport-related consequences. Furthermore, the findings highlight potential gains, including rail networks, mode shifts, and destination choice shifts in network vulnerability assessments.

Visualizing Mobility of Public Transportation System

Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers.

Estimated Value of Savings in Travel Time in Switzerland: Analysis of Pooled Data

Reliable measures of the value of travel time savings (VTTS) are crucial inputs into policy planning in any regional or national context. However, the evidence from secondary or parallel VTTS studies often differs from that from official or national studies. This paper presents evidence from a study aimed at merging evidence from four separate studies of VTTS conducted in Switzerland. The analysis shows the potential of models estimated jointly on the basis of the combined data and produces stable results that should be more representative of those for the overall population.

Estimating Dynamic Workplace Capacities by Means of Public Transport Smart Card Data and Household Travel Survey in Singapore

The number and the temporal and spatial distribution of work locations are crucial information for any transport demand model. To generate the initial transport demand of MATSim, an activity-based multiagent simulation framework, it is necessary to determine dynamic workplace capacities with high spatial resolution, either on a parcel or even a building level. Commonly applied methods to derive work locations are based on census of enterprises information, unemployment insurance database, or combined information of a buildings gross floor area and individual work space requirements. As an alternative, the authors present a methodology that combines public transport smart card transaction data, travel diary surveys, and building information data sources. Work activities are detected from smart card transactions based on observed activity duration and start time and therefore related to public transport stops. To link the observed work activities to individual buildings, a linear programming optimization technique is applied that minimizes the walking time between public transport stops and potential work locations. The method classifies work activities in representative work schedules obtained by clustering methods. Information on maximum allowed building gross floor area derived from land use regulation is combined with estimates on individual work space requirements to ensure that buildings are only assigned with work activities according to their maximal capacity. To account for private transport based work activities, mode shares as observed in a travel diary are taken into account. To demonstrate the applicability, the proposed approach is implemented in Singapore and the results critically reviewed.

Transport modelling in the age of big data

ABSTRACT New Big Data sources such as mobile phone call data records, smart card data and geo-coded social media records allow to observe and understand mobility behaviour on an unprecedented level of detail. Despite the availability of such new Big Data sources, transport demand models used in planning practice still, almost exclusively, are based on conventional data such as travel diary surveys and population census. This literature review brings together recent advances in harnessing Big Data sources to understand travel behaviour and inform travel demand models that allow transport planners to compute what-if scenarios. From trip identification to activity inference, we review and analyse the existing data-mining methods that enable these opportunistically collected mobility traces inform transport demand models. We identify that future research should tap on the potential of probabilistic models and machine learning techniques as commonly used in data science. Those data-mining approaches are designed to handle the uncertainty of sparse and noisy data as it is the case for mobility traces derived from mobile phone data. In addition, they are suitable to integrate different related data sets in a data fusion scheme so as to enrich Big Data with information from travel diaries. In any case, we also acknowledge that sophisticated modelling knowledge has developed in the domain of transport planning and therefore we strongly advise that still, domain expert knowledge should build the fundament when applying data-driven approaches in transport planning. These new challenges call for a multidisciplinary collaboration between transport modellers and data scientists.

Visualizing Waypoints-Constrained Origin-Destination Patterns for Massive Transportation Data

Origin‐destination (OD) pattern is a highly useful means for transportation research since it summarizes urban dynamics and human mobility. However, existing visual analytics are insufficient for certain OD analytical tasks needed in transport research. For example, transport researchers are interested in path‐related movements across congested roads, besides global patterns over the entire domain. Driven by this need, we propose waypoints‐constrained OD visual analytics, a new approach for exploring path‐related OD patterns in an urban transportation network. First, we use hashing‐based query to support interactive filtering of trajectories through user‐specified waypoints. Second, we elaborate a set of design principles and rules, and derive a novel unified visual representation called the waypoints‐constrained OD view by carefully considering the OD flow presentation, the temporal variation, spatial layout and user interaction. Finally, we demonstrate the effectiveness of our interface with two case studies and expert interviews with five transportation experts.

Synthetic Population Generation by Combining a Hierarchical, Simulation-Based Approach with Reweighting by Generalized Raking

A recent approach for generating populations of synthetic individuals through simulation is extended to produce households of grouped individuals. The contingency tables of the generated populations match external controls on the individual and household levels while exhibiting far greater variety in composition than existing approaches can offer. The method involves a two-step approach. The first consists of a procedure based on Gibbs sampling, which has only recently been applied to population generation in transportation modeling and is generically called Markov chain Monte Carlo (MCMC). For this work, the model was generalized, and an extension was developed, hierarchical MCMC, which was able to generate a hierarchical structure. The second step, a postprocessing step, uses generalized raking (GR), which reweights the output from hierarchical MCMC to perfectly satisfy known marginal control totals on the individual and household levels. The application input data—a demographic sample and some known marginals from Singapore—added further complexities to the problem, which had not yet been explored in the current literature. Despite data challenges, consecutively applying the methods above produced realistic synthetic populations. Results confirm their goodness of fit and their generated hierarchical structures.

Introducing the Pedestrian Accessibility Tool

The indexes for walkability proposed so far refer generally to the closest amenities and public transport stops and the existing network structure. The weights of the attributes do not reflect the independently measured preferences of the users and residents. Design attributes such as the location and type of crossings and walkway design features are usually surveyed in walkability audits. However, such attributes are usually not considered when pedestrian walksheds or other accessibility-based walkability indexes are calculated. Nevertheless, these design attributes are very relevant for actual planning decisions. The proposed walkability index can be behaviorally calibrated, has been implemented as a geographic information system tool, and is published as open source software. The pedestrian accessibility tool allows the evaluation of existing and future urban plans with regards to walkability. The tool calculates Hansen-based accessibility indicators with the use of a customizable specification of the generalized walking costs, and it incorporates user-defined weights of destination attractiveness. The basic user workflow of the tool is summarized. Three case studies show real-world applications of the tool to support the planning of pedestrian infrastructure in an urban context. With indications of potential areas of improvement that have been reported by pilot users working in an urban planning department, hints are also given for future research.

Visualizing Transport Futures: The potential of integrating procedural 3d modelling and traffic micro-simulation in Virtual Reality applications

1 In this paper we elaborate on potential use cases of Virtual Reality (VR) in transportation research 2 and planning and present how we integrated procedural 3D modelling and traffic micro-simulation 3 with the rendering capabilities of a game engine in a semi-automated pipeline. 4 Through a review of potential practical applications, we present how this pipeline will be 5 employed to distil behavioural evidence that can guide planners through dilemmas when designing 6 future cycling infrastructure. At the same time, we are studying efficacy of VR as a method for 7 assessing perceptual behaviour as opposed to traditional methods of visualization. Concretely, we 8 present how the pipeline can be adapted i) to generate parameterised visualisations for stated 9 preference surveys, ii) as a platform for a cycling simulator and iii) to communicate different 10 design scenarios for stakeholder engagement. The flexibility of procedural programming allows 11 discretionary changes to the street design and the traffic parameters. Through this experience of 12 developing procedural models, traffic microsimulations and ultimately VR models for streets in 13 Singapore, we find that visual and temporal feedback enabled by VR makes several important 14 design parameters observable and allows researchers to conduct new types of behavioural surveys 15 to understand how people will respond to different design options. In addition, we conclude that 16 such VR applications open new avenues for citizen engagement and communication of urban plans 17 to stakeholders. 18 Erath, Maheshwari, Joos, Kupferschmid and van Eggermond 3

Residential search and location choice in Singapore

1 As many spatial choices, residential location choices are made from a large pool of potential 2 alternatives. This study evaluates choice sets based on households’ search preferences as a new 3 alternative to the more commonly applied random or weighted sampling, using a recent movers 4 survey conducted in Singapore. Residential units are taken as the unit of analysis. 5 Descriptive analysis reveals that households search in a limited area and in a limited number 6 of markets. Subsequently, a choice set generation algorithm is proposed that evaluates the 7 number of alternatives available to a household based on self-reported search preferences. To 8 a large extent the size of the universal choice set is influenced by the temporal and spatial 9 dimension of the search process. 10 Model results are presented that with alternatives sampled from the universal choice set. 11 Additionally, models are presented with choice sets that take into account households’ self12 reported search preferences that include dwelling size, dwelling price and possible areas. Models 13 including spatial variables describing the social environment, combined with choice sets only 14 including alternatives within the preferred price range, perform best. The social environment 15 consisted of variable describing a household’s average distance to work, the distance to their 16 parents and the average distance to the locations where they most frequently meet their five 17 closest contacts. Other significant spatial variables included the distance to a top primary school, 18 as well as the proximity to a mass rapid transit. Given the significance of these variables tt is 19 proposed to further evaluate anchor and distance-based sampling. 20 van Eggermond, M.A.B., Erath, A. and Axhausen, Kay W. 2