Bringing infrastructure into pricing in road freight transportation – A measuring concept based on navigation service data

Kellner, Florian; Otto, Andreas; Brabänder, Christian

doi:10.1016/j.trpro.2017.05.458

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…The anonymization of data also overcomes the privacy concerns mentioned before. Kellner et al [77] used navigation service providers' data to build distance matrices with customers' locations and requested travel times at different times throughout the day. To generalize the approach by Kellner et al [77] and bypass any problems related to subjective trip generation, as for example experienced by Sun et al [85], we use real-world floating car data (FCD) with compressed information collected over time.…”

Section: Road Network Data Collection: Developing a New Methodsmentioning

confidence: 99%

“…One is to measure across the complete network, i.e., from anywhere to anywhere. A second setting measures from defined origins to defined destinations [77], i.e., from somewhere to somewhere. We suggest following a third setting, given an origin, we do not specify a destination and then measure detour and travel speed for the origin-destination pair but specify the origin only and list all destinations that can be reached within a given range or time frame.…”

Section: Fundamentals On Road Network Performance Measurementsmentioning

confidence: 99%

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Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

2020

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Road network performance (RNP) is a key element for urban sustainability as it has a significant impact on economy, environment, and society. Poor RNP can lead to traffic congestion, which can lead to higher transportation costs, more pollution and health issues regarding the urban population. To evaluate the effects of the RNP, the involved stakeholders need a real-world data base to work with. This paper develops a data collection approach to enable location-based RNP analysis using publicly available traffic information. Therefore, we use reachable range requests implemented by navigation service providers to retrieve travel times, travel speeds, and traffic conditions. To demonstrate the practicability of the proposed methodology, a comparison of four German cities is made, considering the network characteristics with respect to detours, infrastructure, and traffic congestion. The results are combined with cost rates to compare the economical dimension of sustainability of the chosen cities. Our results show that digitization eases the assessment of traffic data and that a combination of several indicators must be considered depending on the relevant sustainability dimension decisions are made from.

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Section: Road Network Data Collection: Developing a New Methodsmentioning

confidence: 99%

Section: Fundamentals On Road Network Performance Measurementsmentioning

confidence: 99%

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

2020

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“…Assists the integration and delivery of information about disaster prevention and mitigation [31,32] Geographic Information Systems (GIS)…”

Section: Assists Navigation •mentioning

confidence: 99%

Digital engineering for resilient road infrastructure outcomes: Evaluating critical asset information requirements

Caldera¹,

Mohamed²,

Mostafa³

et al. 2022

J. sustain. dev. energy water environ. syst.

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The United Nations Sustainable Development Goal 9 on building resilient infrastructure highlights the urgent need for enabling evidence-based decision making for Infrastructure Asset Management supported by targeted platforms such as digital engineering and digital earth. In this paper the authors argue that an Asset Information Requirement matrix is an essential decision support tool for authorities and practitioners to evaluate right time, right place use of infrastructure data for resilient outcomes. The authors present an exploratory study that synthesizes the experiences of senior asset management decision-makers from road research institutes, state and local government bodies based in South East Queensland, Australia. The findings are discussed in relation to: digital engineering for managing complex data; current practice and outlook; key asset information requirements; and data structures, interactions and interdependencies. The authors present an 'Asset Information Requirement Matrix' that categorises 66 data requirements across four key infrastructure data types (including 13 information categories), and asserts the relevance of these data requirements for the six key phases of planning, design, construction, acquisition, operations and end-of-life treatment. The authors also present an 'Asset Interaction Matrix' which depicts the temporal, spatial and logical relationships between the 13 information categories. The authors conclude the importance of these asset matrices to leverage digital engineering for resilience infrastructure outcomes. The two matrices create a common language platform for engaging in digital engineering conversations, wherein authorities and practitioners can establish clear arrangements for measuring and monitoring road infrastructure through its life cycle.

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“…We believe that the route recommendation services should be able to provide alternative routes depending on personal preferences, especially ones that recommend health optimal routes. Most routing algorithms used in the navigation services need weights applied to different segments of the road [6]. In a general scenario, the algorithm for calculating the shortest path between two locations would compute the total weight (based on the length of each segment).…”

Section: Introductionmentioning

confidence: 99%

CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

Mahajan

Tang

et al. 2019

IEEE Access

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Transport related pollution is becoming a major issue as it adversely affects human health and one way to lower the personal exposure to air pollutants is to choose a health-optimal route to the destination. Current navigation systems include options for the quickest paths (distance, traffic) and least expensive paths (fuel costs, tolls). In this paper, we come up with the CAR (Clean Air Routing) algorithm and use it to build a health-optimal route recommendation system between the origin and the destination. We combine the open source PM2.5 (Fine Particulate Matter with diameter less than 2.5 micrometers) concentration data for Taiwan, with the road network graph obtained through OpenStreetMaps. In addition, spatio-temporal interpolation of PM2.5 is performed to get PM2.5 concentration for the road network intersections. Our algorithm introduces a weight function that assesses how much PM2.5 the user is exposed to at each intersection of the road network and uses it to navigate through intersections with the lowest PM2.5 exposures. The algorithm can help people reduce their overall PM2.5 exposure by offering a healthier alternative route which may be slightly longer than the shortest path in some cases. We evaluate our algorithm for different travel modes, including driving, cycling and walking. An analysis is done for more than 4,000 real-world travel scenarios. The results show that our approach can lead to an average exposure reduction of 17.1% with an average distance increase of 2.4%. INDEX TERMS Routing, smart navigation, urban computing, air quality.

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Bringing infrastructure into pricing in road freight transportation – A measuring concept based on navigation service data

Cited by 12 publications

References 9 publications

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements

Digital engineering for resilient road infrastructure outcomes: Evaluating critical asset information requirements

CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

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