Wasim Shoman

Road transport accounted for 20% of global total greenhouse gas emissions in 2020, of which 30% come from road freight transport (RFT). Modeling the modern challenges in RFT requires the integration of different freight modeling improvements in, e.g., traffic, demand, and energy modeling. Recent developments in 'Big Data' (i.e., vast quantities of structured and unstructured data) can provide useful information such as individual behaviors and activities in addition to aggregated patterns using conventional datasets. This paper summarizes the state of the art in analyzing Big Data sources concerning RFT by identifying key challenges and the current knowledge gaps. Various challenges, including organizational, privacy, technical expertise, and legal challenges, hinder the access and utilization of Big Data for RFT applications. We note that the environment for sharing data is still in its infancy. Improving access and use of Big Data will require political support to ensure all involved parties that their data will be safe and contribute positively toward a common goal, such as a more sustainable economy. We identify promising areas for future opportunities and research, including data collection and preparation, data analytics and utilization, and applications to support decision-making.

Electric road systems (ERS)—infrastructure that allows for charging while driving—are currently considered in Sweden for electrifying long-haul trucking. The technology can also charge battery electric passenger vehicles (BEVs). This study utilizes real-world car movement data in Sweden and detailed spatial analysis to explore to what extent ERS could displace stationary charging if it is available for BEVs and the expected benefits. We find that ERS utilization and the minimum battery ranges depend more on visited locations and home locations and less on the annual travel distances of car users. The median battery ranges required by rural residents are 15–18% greater than for urban residents. Our scenarios suggest that a mix of ERS and home-charging would achieve the most significant benefits. ERS with home charging reduces the required battery range by 62–71% in the main scenarios, and the net savings from smaller BEV batteries exceed the cost of ERS. Eliminating all stationary charging is feasible for many but not all vehicles. Utilizing ERS could also significantly reduce peak BEV charging by distributing charging throughout the day. We also find that there is a considerable difference between the maximum possible and minimum needed charging on ERS, which can significantly influence ERS revenues. Future studies can expand to include other modes (e.g., trucks) to provide more holistic assessments of economic benefits and charging needs.

This research investigates carbon footprint impacts combination with different charging conditions, including electric road system (ERS) that enables dynamic on-road charging. The research applies a prospective life cycle analysis framework for estimating carbon footprints of vehicles, fuels, and infrastructure. The framework includes vehicle stock turnover modeling of fleet electrification and modeling of optimal battery capacity for different charging conditions based on Swedish real-world driving patterns. All new car sales are assumed to be electric after 2030 following phase-out policies for gasoline and diesel cars. Implementing ERS on selected high-traffic roads could yield significant avoided emissions in battery manufacturing compared to the additional emissions in ERS construction. ERS combined with stationary charging could enable additional reductions in the cumulative carbon footprint of about 12-24 million tons of CO2 over 30 years (2030-2060) compared to an electrified fleet only relying on stationary charging. The range depends on uncertainty in emission abatement in global manufacturing, where the lower is based on Paris Agreement compliance and the higher on current climate policies. A large share of the reduction could be achieved even if only a small share of the cars adopts the optimized battery capacities.

Earlier location-based accessibility analysis by car mode utilizes network travel distance (NTD) or static travel time (STT) as travel impedances. A recent trend in the literature that considers impedance as dynamic travel time (DTT) emerged, allowing examining continuous accessibility patterns. This research aims to evaluate the measured spatial-temporal change in accessibility for the different travel impedances. The research develops a GIS-remote sensing framework to evaluate the resemblance in spatial and temporal accessibility change at high resolution. It also investigates the dominant impact of significant accessibility components on influencing their results. According to results, impedances of NTD and STT represent almost identical outcomes, with correlation coefficients almost equal to 1.00, while failing to capture the dominant change of accessibility components. However, DTT illustrates varying patterns highly influenced by change in mobility. It shows the interplay between dominant accessibility components at the required resolution, which could facilitate better knowledge-based decisions for policymakers.

High budget transport infrastructure projects in the Istanbul Metropolitan Area endeavour to increase the efficiency of the transportation system. Among those investments; the bus rapid transit system -Istanbul Metrobus Line (IML) - is the most popular one that serves one million trips per day. Yet, the performance and impact of IML are not quantitatively assessed. Hence in this study, a high-resolution GIS-remote sensing framework is designed to analyse before and after accessibility over the period of forty years (namely 1987-1997-2007-2014). High resolution satellite images were processed to generate the lacking historical land cover/use information with the expected high accuracy. Within the study area, utilized accessibility indices have unevenly increased after the operation of IML, such as 104.19% in potential and 99.07% in daily accessibility. According to the achieved results, such high spatio-temporal spatial framework could aid decision makers to quantitatively assess and evaluate the performance of such investments.

For spatial analyses, raster land cover/use maps are converted into points, where each point holds attribute of its corresponding land cover/use. However, these are not identical in terms of areas or shapes; thus assigning a point to each isolated shape is not an adequate solution and for that gridding is suggested. Square, hexagon and triangle are among the basic land use gridding systems where each of them has its own advantages in such process. This research aims to compare the systems in providing accurate representations of the original land cover/use maps, assess the data loss while increasing resolution and suggest suitable gridding system. The research finds the errors in area and feature numbers as criteria for selected classes. Modules that find out errors in each scale considering each criterion and class alone are proposed. The modules suggest both the best system for each criterion alone and for combined criteria.

This paper proposes a compound hierarchy to be used in cartographic labelling of streets. The main purposes of this hierarchy are to ease navigation of street maps and to provide more understandability and legibility of street features for map users. The hierarchy uses centrality measures along with functional classes of the streets to order features according to their spatial importance in each used zoom level. The calculation of a proper radius, to be employed in the calculations of centrality measures, is based on extracting proper field of view in the map user’s focus status. The used measures are integrated using fuzzy-analytic hierarchy process, yielding proper coefficients to create the hierarchy. The hierarchy is applied for the thinning process and later for using the styled layer descriptor to label street features. Quality constrains are implemented to make the street labels more understandable and readable.

This paper introduces a new hierarchy for cartographic generalisation processes, applied in street networks. The aims of implementing this hierarchy are to emphasise on significant street features, and to provide more free spaces between street features. The hierarchy is obtained from the functional classes of the features and four centrality measures in a street network, i.e. betweenness, reach, straightness and closeness extracted from a primary graph. The values of centrality measures change in every zoom level by calculating a radius parameter, which depends on the users’ field of view. The coefficients for the measures are constructed using a decision-making technique called fuzzy analytical hierarchy process (FAHP). The weights for each of the centrality measures are computed and normalised to form the proposed hierarchy. The hierarchy is applied and used later in the thinning process to omit insignificant features from the street network in medium scales.