Denna rapport är en sammanfattning av metodik och resultat från forskningsprojektet "Datadriven restidsanalys" som pågått maj 2023 –april 2025. Syftet med projektet har varit på att utveckla och utvärdera nya datadrivna metoder och analysverktyg för att identifiera och förutse potentiella brister i vägnätet. Tyngdpunkten ligger på restidsdata från vägfordon från Trafikverkets system Stress, för att ge en nyanserad och proaktiv bild av framkomligheten i vägtrafiksystemet.

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.

We propose a methodology to identify and analyze similarities in long-term trends of road links using travel speed data. The methodology employs seasonal trend decomposition by LOESS (STL) to extract trend curves from travel speed time series, clearly representing underlying long-term patterns and behavior. These trend curves are then analyzed using the k-means clustering algorithm to group road links based on long-term trends. The resulting clusters offer valuable insights for long-term planning in traffic management, infrastructure development, and identifying potential bottlenecks within the road network. To demonstrate the proposed methodology, we applied it to travel speed data from the European road E4, focusing on the route between Södertälje and Stockholm. The analysis reveals distinct trend characteristics and behaviors, highlighting the diverse nature of traffic patterns in different road links.

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. 

Traffic analysis is vital for enhancing the performance of transportation systems, where continuous evaluation of traffic states helps responsible road authorities detect and address issues. High-quality traffic data is key to analysis, as it aids in planning and investments. Traditionally, traffic data collection has been costly and limited. Nowadays, connected vehicles and mobile phones have transformed this process, enabling traffic data collection across large geographic regions without the need for dedicated measurement devices. The availability of large-scale and detailed traffic data allows for in-depth analysis using mathematical models. This thesis develops models to utilize available traffic data for transportation system improvements, aiming to enhance traffic conditions and road user experience. It utilizes data from link flows and travel times, applying models over large geographic areas. The thesis addresses transportation engineering issues through data-driven methods. The thesis proposes two methods for allocating electric vehicle charging stations using optimization and route sampling techniques. It introduces a new index for assessing travel time reliability. It shows how clustering analysis of descriptive travel time statistics can be used to detect different traffic states. Furthermore, this thesis presents a statistical model to estimate link flow propagation using measured link flow data, analyzing traffic influence across surrounding areas. The thesis also uses traffic simulation, focusing on combining speed cameras and probe vehicles for data collection and developing a model to identify probable routes based on hourly link flows. The thesis results highlight the importance of data-driven models in optimizing transportation systems and improving road user travel experiences.

Characterizing links in road networks is vital for understanding recurring traffic state patterns. For long-term planning, clustering can reveal links with similar characteristics and patterns that may indicate degraded performance 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. Our study uses different clustering techniques on descriptive statistics to categorize link-types and day-types. Applying our method to high-resolution travel speed data, reveals consistent link characteristics across different clustering algorithms. The preliminary results show that the identified clusters maintain stability both in space and time, confirming their effectiveness in identifying consistent link characteristics and daily patterns. This offers insights into traffic state variations based on travel speed.

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

Det uppkopplade samhället möjliggör kontinuerlig insamling av trafikdata genom de moderna fordonens navigationssystem, så väl som genom mobiltelefoner och andra GPS-enheter. Den ökade tillgången till trafikdata ger möjligheter analysera att trafiksystemet ur flera olika perspektiv. En typ av trafikdata som kan extraheras ur GPS-data är fordonshastigheter. Genom att analysera hur fordonshastigheter förändras över tid och identifiera avvikelser från ett normaltillstånd så skulle det vara möjligt att upptäcka och förutsäga potentiella brister i trafiksystemet. Många gånger upptäcks brister i infrastrukturen i ett relativt sent skede vilket i sin tur kan innebära både omfattande och kostsamma åtgärder för att komma till rätta med problemen.

Syftet med förstudien har varit att utveckla och utvärdera metoder och modeller för att detektera brister och identifiera hastigheter som relativt avviker från normaltillståndet, dvs ett fokus på fordonshastigheter som framför allt är ovanligt låga. Utgångspunkten har också varit att finna lämplig metod för att modellera trafiksituationen med hjälp av uppmätta fordonshastigheter. Det vill säga metoder som syftar till att ur ett övergripande perspektiv beskriva normaltillståndet längs med de studerade vägsträckorna. Analyser av normaltillståndets förändring över tid öppnar upp möjligheten att detektera om brister relaterat till vägsträckors kapacitet och framkomlighet uppstått eller avgöra om normaltillståndet på en vägsträcka är stabilt eller förändras över tid.

Det är framför allt de relativt låga fordonshastigheterna som uppstår som blir en indikator på att en vägsträcka har brister. Därför föreslås en metod för att systematiskt identifiera och gruppera uppmätta fordonshastigheter i låga, normala och höga hastigheter. En utgångspunkt har varit robusthet och att möjliggöra jämförelser av hastigheter för olika vägsträckor med olika attribut som antal körfält och skyltad hastighet med varandra. Vi presenterar även ett nytt mått som beskriver hur gruppen med relativt låga hastigheter förhåller sig till friflödeshastigheten som till exempel den skyltade hastigheten. Syftet med måttet är att kvantifiera framkomligheten på en vägsträcka eller vägsegment. Existerande mått och indikatorer baseras idag på fordonshastigheter som spänner från låga till höga hastigheter. Vi har i denna kontext tagit fram ett mått som endast tar hänsyn till vad som anses vara låga hastigheter och friflödeshastighet.

Inom förstudien så har även en metod baserad på klusteranalys använts för de studerade vägsträckorna. Klusteranalys har i olika sammanhang visat sig effektivt för att kategorisera och detektera återkommande mönster i hastighetsprofiler. Syftet med klusteranalysen är att undersöka om det finns någon koppling mellan hastighetsprofiler som har liknande beteende och till exempel veckodag och tidpunkt. Genom klusteranalys skulle det vara möjligt att inte bara detektera vilka vägsträckor där det uppstår problem, utan det skulle även vara möjligt att prognostisera vid vilka veckodagar och tidpunkter där det finns risk att köer och andra problem kan uppstå.

Förstudien är begränsad till användning av fordonhastigheter som datakälla och de framtagna metoderna och modellerna visar att det finns potential att frikoppla sig från andra datakällor som till exempel fordonsflöden för att detektera brister eller avvikelser som skulle kunna indikera brister i transportsystemet.

Modern vehicles are to a large extent connected today, either directly by built-in navigation systems in the vehicles or indirectly by other devices such as mobile phones and GPS units. This enables the possibility to continuously collect traffic data in a cost-effective way. The increased access to detailed data allows practitioners and researchers to analyze the transportation system from various perspectives. The travel speed is a common descriptor of the traffic state, and it can be extracted from GPS data. By analyzing how the travel speed vary over time and detect anomalies among the measured travel speeds, it is possible to detect potential deficiencies in the transportation system, e.g., insufficient road capacity which may cause bottlenecks. Often, a weakness in the infrastructure is detected in a very late stage which means that extensive investments may be required to resolve the deficiency.

The purpose of the pilot study is to develop methods and models to detect deficiency in the transportation system and to identity travel speeds that deviates from the normal state, i.e., travel speeds that are considered as very low or very high with respect to the normal behavior. Thus, the starting point of the pilot study is to find appropriate ways to model the traffic state along the studied road segments by using measured travel speeds from a general point of view. Analysis of the traffic state allows the study of how the normal state of the road segments change of time to detect deficiency related to road capacity and road access which may occur if no changes are made, or to detect road segments where the normal state is unchanged.

Typically, slower travel speeds may be an indicator of that a deficiency along a road segment exists. Thus, we present a method to systematically partition measured travel speeds in low, normal, and high travel speeds. The method is robust and enable the possibility to compare different road segment with different attributes, such as number of lanes and free-flow travel speed, with each other. Furthermore, we present a new measurement to describe how the low travel speeds relates to the free flow travel speed, e.g., the speed limit. Existing measurements and indicators used today utilize travel speeds which range from low to high. Our proposed measurement uses low travel speed and free flow travel speed exclusively and aims to quantify the accessibility and condition of a road segment.

The pilot study also includes an initial attempt to apply cluster analysis to detect recurrent patterns along the studied road segments. Cluster analysis is in several contexts an effective method to group time series to detect recurrent patterns among the speed profiles. The purpose of using cluster analysis is to evaluate if speed profiles with similar behavior is related to, for instance, weekday or time of the day. Thus, cluster analysis may be used to detect road segments with recurring low travel speeds, and potentially be used to forecast when congestion or queues may occur.

The pilot study is mainly limited to travel speed data. The proposed methods and models show that it is possibly to solely use travel speed data to detect deficiencies in the transportation system. In particular, the pilot study shows the potential to detect deficiencies in the transportation system without additional data sources such as link flow data.

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.