David Kossowsky

Bio: David Kossowsky is an academic researcher from Esri (Canada). The author has contributed to research in topics: Sustainable transport & Smart city. The author has an hindex of 1, co-authored 2 publications receiving 6 citations.

Evaluating pedestrian perceptions of street design with a 3D stated preference survey

Abstract: In many places, streets are still primarily designed for the convenience of motorists, considering mobility function as the principal design goal. There is a scarcity of empirical evidence on the r...

iCity: big data and visualization urban transportation strategies

Abstract: Providing efficient, cost-effective, sustainable transportation networks and services is a major challenge for cities around the world not only for individual cities, but for connectivity between cities. High quality transportation services, notably well-designed transit hubs within comprehensive networks are fundamental prerequisites for effective cities and spur economic, social and cultural inclusion, development and growth. Transportation strategies must be at the heart of smart city strategies. The melding of machine learning, simulations, predictive analytics and design create capacity and connectivity that will help policy and makers gain insight into complex decision-making processes and support evidence-based decision making. Solving transportation and transit challenges requires integrating transdisciplinary knowledge, including computer science, engineering into city planning.

Perceived Safety and Pedestrian Performance in Pedestrian Priority Streets (PPSs) in Seoul, Korea: A Virtual Reality Experiment and Trace Mapping.

Abstract: Pedestrian Priority Street (PPS) project, launched to encourage safer and more convenient walking by improving the inferior pedestrian environment on narrow streets without sidewalks, is based on Monderman’s shared space concept. Similar to the shared space approach, PPS aims for mutual consideration between pedestrians and drivers and strives to create a pedestrian-friendly environment, but the project relies on a unique road surface design. Considering the two main goals of the PPS project, this study investigated how subjective safety and pedestrians’ movements differed by design types. To analyze safety perception, ordered Logit regression and post-hoc interviews were conducted with visual assessment survey using recorded VR (virtual reality) videos. Next, trace mapping and analysis were performed based on the video recordings to measure the degree of free walking. The results found that pedestrians perceived higher safety level in PPSs than in general back road. Further, the pedestrians moved more freely in the street with an integrated design. In other types, which suggested a pedestrian zone at the roadside, there was not much difference in behavior from the general back roads. Thus, the design principle of PPS, which does not set a boundary between pedestrian and vehicle area, should be observed to lead to behavioral changes in pedestrians.

Knowledge-Assisted Visualization of Multi-Level Origin-Destination Flows Using Ontologies

Abstract: Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.

Stated Preference Survey for Estimating Passenger Transfer Penalties: Design and Application to Madrid

Abstract: Transfers in multimodal urban trips imply a disutility for travellers, who perceive them as a penalty when using public transport. It is therefore important to estimate the utility associated to transfers and the main factors affecting it, to allow policymakers to reduce users’ perceived disutility and enhance their intention to use public transport. The aim of this paper is to develop a Stated Preference experiment to estimate the penalty perceived by commuters (workers and students) when making transfers in multimodal urban trips. A web-based survey combining a Revealed Preferences and Stated Preferences survey was created using Ngene software, and an efficient design was applied to estimate multinomial logit models. We present here the first stage of the research: the design and results of the pilot survey of commuters travelling by metro or urban bus in the city of Madrid, Spain. The findings reveal a pure transfer penalty, independent of in-vehicle time, walking and waiting time and crowding. This pure penalty increases with the number of transfers. Crowded transfers cause a high disutility for commuters, which rises with the number of transfers in the total trip. This paper highlights the importance of conducting a pilot survey when designing the final survey. Transfer penalties vary between cities, so pilot studies are encouraged to obtain more accurate results. Further research is needed to consolidate the pilot results with those of a final survey.