Electric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden.

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

Travel times and travel time reliability are important indicators of the traffic state in a transportation system. Analyzing travel times and their reliability and how they vary over time makes it possible to identify existing road network deficiencies or deficiencies that may eventually occur unless necessary actions are taken. Road network deficiencies, such as inadequate road capacity or substandard road design, typically lead to congestion and trip delays for road users. A novel median-based misery index is proposed to highlight potential road network deficiencies in the transportation system. The proposed index offers a way to gauge travel time reliability, providing valuable insights into roads where improvements may be needed. The newly developed median-based misery index operates by computing the relative disparity between slow travel speeds and free-flow travel speeds. The median-based misery index is more robust to skewed distributions of travel times than the ordinary misery index. Our empirical case study includes a spatiotemporal analysis of travel speed data from a section of the European route E4 in Sweden. The case study results show that the new index may be used to detect peak periods when the traffic conditions for road users have deteriorated.

Characterizing link-types and day-types in road networks is vital for understanding recurring traffic state patterns. Link-types and day-types in road networks describe road segments and days based on their specific characteristics.For long-term planning, clustering can be used to categorize links and days with similar characteristics and patterns that may indicate degraded performance in the road network in the future. In this paper, we apply cluster analysis to automate this process and identify similarities among links and days to find potential infrastructure deficiencies and recurring traffic states. Our study uses k-means on descriptive statistics to reveal link-types and day-types. Applying our method to high-resolution travel speed data from a road in Sweden reveals distinct characteristics based on the link and day. The results indicate that the relative difference between the measured travel speed and the free-flow travel speed is negative on links with higher free-flow travel speeds. Additionally, the variability in travel speeds is greater on links with lower free-flow travel speeds.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region. 

The process of collecting traffic data is a key component to evaluate the current state of a transportation network and to analyze movements of vehicles. In this paper, we argue that both active stationary and mobile measurement devices should be taken into account for high-quality traffic data with sufficient geographic coverage. Stationary devices are able to collect data over time at certain locations in the network and mobile devices are able to gather data over large geographic regions. Hence, the two types of measurement devices have complementary properties and should be used in conjunction with each other in the data collection process. To evaluate the complementary characteristics of stationary and mobile devices for traffic data collection, we present a traffic simulation model, which we use to study the share of successfully identified vehicles when using both types of devices with varying identification rate. The results from our simulation study, using freight transport in southern Sweden, shows that the share of successfully identified vehicles can be significantly improved by using both stationary and mobile measurement devices.

Traffic flow estimates play a key role in traffic network management and planning of transportation networks. Commonly it is the average daily traffic (ADT) flow for different road segments that constitute the data. This paper shows how an advanced and detailed analysis based on hourly flow measurements over the day can contribute to a deeper understanding of how hourly flows together reflect the vehicles' routes. The proposed method identifies the shortest travel time paths between all possible origins and destinations in a transportation network, and thereafter it identifies the most significant routes in the network by performing statistical tests. For this purpose, the paper presents a mathematical model, a vehicle simulator based on this model, and a statistical framework that is able to find the most probable underlying routes. The paper contains a real test scenario based on 24-hour traffic flows (hour by hour) to demonstrate the applicability of the method. © 2020 IEEE.