Showing papers by "Sidharta Gautama published in 2019"

A collaborative stakeholder decision-making approach for sustainable urban logistics

Abstract: Cities strongly rely on efficient urban logistics to ensure their attractiveness, quality of life, and economic development. In the same time, they strive to ensure livable and safe environments around its road network, where the increased presence of light and heavy goods vehicles raises questions of regarding safety and environmental impacts. Recent literature has well-recognized the need to consider different stakeholders’ perspectives on these issues, in order to achieve desired outcomes. In this paper, we introduce a collaborative stakeholders’ decision-making approach for sustainable urban logistics, and demonstrate its applicability on a real-life example. The suggested approach extends existing route planning approaches by considering route sustainability as a part of an arc’s traversal cost. The integration of route sustainability is based on the adoption of a multi-criterial decision-making approach, with the possibility of including different stakeholders’ points of view, and evaluating the sustainability cost concerning the route’s spatial context. To demonstrate the applicability of the suggested approach, we extract the route sustainability cost from the traffic sign database, and implement the findings on a real-life example. Furthermore, the suggested approach exhibits a high level of transferability to various local contexts, where local stakeholders might have a different view on the route sustainability than is the case in our example

A review of the internet of floods: Near real-time detection of a flood event and its impact

Abstract: Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future.

Error Sources in the Analysis of Crowdsourced Spatial Tracking Data

Abstract: Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. To improve the reliability of reporting in such mobility studies, this paper systematically analyses the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information.

Exploring the issue of integrating logistics and traffic control in urban areas

Abstract: This paper proposes an optimization framework for urban transportation networks’ (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs.

Traffic management as a service

Abstract: This paper presents Traffic Management as a Service (TMaaS), a neutral traffic management framework for urban mobility aimed at small and medium-sized cities. It accepts and connects multimodal data and services from different parties for monitoring, analysis and management and allows flexible adaptation and on-demand use of the system. TMaaS is an open urban traffic management marketplace that enables third parties to generate innovative solutions and business models and encourages citizen participation and co-creation in urban mobility. TMaaS is currently being demonstrated in real-use cases for the City of Ghent, a medium-sized city in Belgium. Selected replicator cities will be included in 2020.

Detection of points of interest from crowdsourced tourism data

Abstract: Availability of the big data on human mobility raised a lot of expectations regarding the possibility to have a more detailed insights into daily and seasonal mobility patterns. However, this is not a trivial task and often noisy positioning data pose a great challenge among researchers and practitioners. In this paper, we tackle the detection of the Points of Interest (PoI) locations from the mobile sensed tourist data gathered in Zeeland (Netherlands) region. We consider different clustering approaches to detect individuals and collective PoI locations and find that OPTICS proved to be the most robust against initial parameters choices and k-means the most sensitive. K-means also seemed not appropriate to use to extract individual places but it indicates promising to extract areas of city which are often visited.