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GPS-based data are becoming a cornerstone for real-time transportation applications. Tracking data of vehicles from GPS receivers are however susceptible to measurement errors. The assessment of the reliability of data from GPS receiver is a neglected issue, especially in a real road network setting and in the phase after data transfer but before information identification. An evaluation method is outlined and carried out by conducting a randomized experiment. We assess the reliability of GPS-based transportation data on geographical position, speed, and elevation from three varied receivers GlobalSat BT-338X, Magellan SporTrak Pro and smart phone for three transportation modes: bicycle, car, and bus. The positional error ranging from 0158 meters, and 74% to 100% with an error within 5 meters depending on the transportation mode and route, there is also a non-negligible risk for aberrant positioning. Speed is slightly underestimated or overestimated with errors around 5km/h except for SporTrak Pro which had an error

The road network is a necessary component in transportation. It facilitiesspatial movements of people and goods, and it also influences the optimal locations of facilities that usually serve as destinations of the movements. To fulfill the transportation needs and to adapt to the facility development, the road network is often organized hierarchically and asymmetrically with various road levels and spatial structures. The complexity of the road network increases along with the increase of road levels and spatial structures. However, location models locate facilities on a given road network, usually the most complex one, and the influence from the complexity of road network in finding optimal locations is not well-studied. This paper aims to investigate how the complexity of a road network affects the optimal facility locations by applying the widely-applied p-median model. The main result indicates that an increase in road network complexity, up to a certain level, can obviously improve the solution, and the complexity beyond that level does not always lead to better solutions. Furthermore, the result is not sensitive to the choice of algorithms. In a specific case study, a detailed sensitivity analysis of algorithm and facility number further provides insight into computation complexity and location problems from intra-urban to inter-urban.

The advancement of GPS technology has made it possible to use GPS devices as orientation and navigation tools, but also as tools to track spatio-temporal information. GPS tracking data can be broadly applied in location-based services, such as spatial distribution of the economy, transportation routing and planning, traffic management and environmental control. Therefore, knowledge of how to process the data from a standard GPS device is crucial for further use. Previous studies have considered various issues of the data processing at the time. This paper, however, aims to outline a general procedure for processing GPS tracking data. The procedure is illustrated step by step by the processing of real-world GPS data of car movements in Borlänge in the centre of Sweden. 

In a city there are hotspots that attract the citizens and most of the transportation in the city arises when citizens move between their residence and the hotspots. However, the evaluation between energy-efficient mobility and urban residential planning has been found to be rather weak. In this paper, we propose an ex-ante evaluation method to quantify the impacts in terms of CO₂ emissions induced by intra-urban car mobility due to different residential plans implemented at various urban areas. The method is illustrated by a Swedish midsize city which is presently preoccupied with urban planning of new residential areas in response to substantial population growth due to immigration. On average, the CO₂ emissions increase from the continued urban core area (CUCA) to the suburban polycentric area (SPA) to the edge urbanization area (EUA), EUA is almost 3 times more than CUCA. The average travel distances also increase in the same sequence, the overall increase is more than 4 times. Apartment buildings could be more effective in meeting residential needs and mitigating CO₂ emissions than dispersed single-family houses.

There are three key actors in forming the sustainable spatial distribution of residency in an area, (local) government, the estate investor and the resident, each with its own objective. Most urban planning studies have mainly focused on the ex-post evaluation of residential development by considering the objective of each actor separately. This paper outlines a conceptual model where the three key actors and their unique objectives are integrated with the aim of providing an ex-ante evaluation of residential development for government to make policies operational on a micro level. The methodology is implemented on a Swedish city, where sustainable residential development is in high need due to the influx of immigrants. The case study demonstrates that the model can integrate the macro and micro actors well. The model can provide noteworthy insights for the government on where the objectives of sustainability, livability and profit can be met. A sensitivity check of the parameter settings shows that the implementation of the model is robust for replication in other cities.