Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

Worldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

Hindrances in port digitalization? Identifying problems in adoption and implementation

Digitalization has an impact on all domains of maritime transport and logistics. Ports’ ability to act as a part of digital networks and information chains is vital for its competitiveness. This requires means and prerequisites to integrate with contemporary technology platforms and system architectures. Such readiness should exist in different parallel processes taking place in organizations of port communities. Successful digitalization requires focused technology management ensuring system and data transfer interoperability. The paper addresses problems, obstacles, and hindrances that ports are currently facing in their digitalization efforts. Interoperability and stakeholder interaction is significant, particularly between the port management, municipal ownership, and business operators and vendors. In the contemporary port development, environmental regulations have an effect on the level and effectiveness of digitalization. The future development of port digitalization will be dependent on the port capabilities to adopt and implement reliable and adoptable technologies with clear vision of the future.

Estimated crash avoidance with the hypothetical introduction of automated vehicles: a simulation based on experts’ assessment from French in-depth data

Objective We aimed to quantify, through simulations using real crash data, the number of potentially avoided crashes following different replacement levels of light vehicles by level-5 automated light vehicles (AVs). Methods Since level-5 AVs are not on the road yet, or are too rare, we simulated their introduction into traffic using a national database of all fatal crashes and 5% of injury crashes observed in France in 2011. We fictitiously replaced a certain proportion of light vehicles (LVs) involved in crashes by level-5 AVs, and applied crash avoidance probabilities estimated by a number of experts regarding the capabilities of AVs depending on specific configurations. Estimates of the percentage of avoided crashes per user configuration and according to three selected (10%, 50%, 100%) replacement levels were made, as well as estimates taking into account the relative weight of these crash configurations, and considering fatal and injury crashes separately. Results Our simulation suggests that a reduction of almost half of fatal crashes (56%) and injury crashes (46%) could be expected by replacing all LVs on the road with level-5 AVs. The introduction of AVs would be the least effective for crashes involving a vulnerable road user, especially motorcyclists. Conclusion This result represents encouraging prospects for the introduction of automated vehicles into traffic, while making it clear that, even with all light vehicles replaced with level 5-AVs, all issues would not be solved, especially for crashes involving motorcyclists, cyclists and pedestrians.

A systematic literature review of ride-sharing platforms, user factors and barriers

Aim Ride-sharing is an innovative on-demand transport service that aims to promote sustainable transport, reduce car utilization, increase vehicle occupancy and public transport ridership. By reviewing ride-sharing studies around the world, this paper aims to map major aspects of ride-sharing, including online platforms, user factors and barriers that affect ride-sharing services, and extract useful insights regarding their successful implementation. Method A systematic literature review is conducted on scientific publications in English language. Articles are eligible if they report a study on user factors affecting ride-sharing use and/or barriers preventing ride-sharing implementation; ride-sharing online platforms in these articles are also recorded and are further explored through their official websites. A database is built that organizes articles per author, year and location, summarizes online platform attributes, and groups user factors associated with the likelihood to ride-share. Findings The review shows that the term “ride-sharing” is used in the literature for both profit and non-profit ride-sharing services. In total, twenty-nine ride-sharing online platforms are recorded and analyzed according to specific characteristics. Sixteen user factors related to the likelihood to ride-share are recorded and grouped into sociodemographic, location and system factors. While location and system factors are found to follow a pattern among studies, mixed findings are recorded on the relationship between sociodemographic factors and ride-sharing. Factors that may hinder the development of ride-sharing systems are grouped into economic, technological, business, behavioral and regulatory barriers. Conclusion Opportunities exist to improve the quality of existing ride-sharing services and plan successful new ones. Future research efforts should focus towards studying ride-sharing users' trip purpose (i.e., work, university, shopping, etc.), investigating factors associated to ride-sharing before and after implementation of the service, and perform cross-case studies between cities and countries of the same continent to compare findings.

Exploring injury severity of pedestrian-vehicle crashes at intersections: unbalanced panel mixed ordered probit model

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes.

On how to incorporate public sources of situational context in descriptive and predictive models of traffic data

Background European cities are placing a larger emphasis on urban data consolidation and analysis for optimizing public transport in response to changing urban mobility dynamics. Despite the existing efforts, traffic data analysis often disregards vital situational context, including large-scale events, weather factors, traffic generation poles, social distancing norms, or traffic interdictions. Some of these sources of context data are still private, dispersed, or unavailable for the purpose of planning or managing urban mobility. Addressing the above observation, the Lisbon city Council has already established efforts for gathering historic and prospective sources of situational context in standardized semi-structured repositories, triggering new opportunities for context-aware traffic data analysis. Research questions The work presented in this paper aims at tackling the following main research question: How to incorporate historical and prospective sources of situational context into descriptive and predictive models of urban traffic data? Methodology We propose a methodology anchored in data science methods to integrate situational context in the descriptive and predictive models of traffic data, with a focus on the three following major spatiotemporal traffic data structures: i) georeferenced time series data; ii) origin-destination tensor data; iii) raw traffic event data. Second, we introduce additional principles for the online consolidation and labelling of heterogeneous sources of situational context from public repositories. Third, we quantify the impact produced by situational context aspects on public passenger transport data gathered from smart card validations along the bus (CARRIS), subway (METRO) and bike sharing (GIRA) modes in the city of Lisbon. Results The gathered results stress the importance of incorporating historical and prospective context data for a guided description and prediction of urban mobility dynamics, irrespective of the underlying data representation. Overall, the research offers the following major contributions: A novel methodology on how to acquire, consolidate and incorporate different sources of context for the context-enriched analysis of traffic data; The instantiation of the proposed methodology in the city of Lisbon, discussing the role of recent initiatives for the ongoing monitoring of relevant context data sources within semi-structured repositories, and further showing how these initiatives can be extended for the context-sensitive modelling of traffic data for descriptive and predictive ends; A roadmap of practical illustrations quantifying impact of different context factors (including weather, traffic interdictions and public events) on different transportation modes using different spatiotemporal traffic data structures; and A review of state-of-the-art contributions on context-enriched traffic data analysis. The contributions reported in this work are anchored in the empirical observations gathered along the first stage of the ILU project (see footnote 1), providing a study case of interest to be followed by other European cities.

Video observation of encounters between the automated shuttles and other traffic participants along an approach to right-hand priority T-intersection

Introduction Increasing numbers of deployment projects of automated shuttles have been taking place worldwide. Safety is one of the main concerns for their successful implementation. Therefore, it is vital to gain the knowledge about interactions between these shuttles and other traffic participants. Method Given the lack of behavioural observational studies under regular traffic conditions, the presented study applies external video recordings to explore encounters between the shuttles approaching a T-intersection and other traffic participants. The encounters of interest included a vulnerable road user in the bicycle lane, a pedestrian on the zebra crossing and a road user overtaking the shuttle. The shuttles were identified from the video by RUBA software. We analysed the encounters using T-Analyst software together with the manual observation of traffic participants' behaviour. Results From 220 h of video, 318 unique manoeuvres of the shuttle were observed and 83 encounters with other traffic participants were identified and explored. Several types of risks and behavioural patterns were identified, such as road users misusing the defensive style of the shuttles or cyclists in the bicycle lane not being sure about the shuttle’s intention. Frequent hard stops of the shuttles might be dangerous for the passengers inside and can increase the risk of rear end accidents. Conclusions The findings provide a valuable insight into the interactions between automated shuttles and other traffic participants under regular traffic conditions on one location in Oslo, Norway. The study showed that introducing automated shuttles into regular traffic can lead to the emergence of new types of interactions between the shuttles and other traffic participants.

Analysis and comparison of traffic flow models: a new hybrid traffic flow model vs benchmark models

Background This paper compares a hybrid traffic flow model with benchmark macroscopic and microscopic models. The proposed hybrid traffic flow model may be applied considering a mixed traffic flow and is based on the combination of the macroscopic cell transmission model and the microscopic cellular automata. Modelled variables The hybrid model is compared against three microscopic models, namely the Krauß model, the intelligent driver model and the cellular automata, and against two macroscopic models, the Cell Transmission Model and the Cell Transmission Model with dispersion, respectively. To this end, three main applications were considered: (i) a link with a signalised junction at the end, (ii) a signalised artery, and (iii) a grid network with signalised junctions. Results The numerical simulations show that the model provides acceptable results. Especially in terms of travel times, it has similar behaviour to the microscopic model. By contrast, it produces lower values of queue propagation than microscopic models (intrinsically dominated by stochastic phenomena), which are closer to the values shown by the enhanced macroscopic cell transmission model and the cell transmission model with dispersion. The validation of the model regards the analysis of the wave propagation at the boundary region.

Transport behavior-mining from smartphones: a review

Background Although people and smartphones have become almost inseparable, especially during travel, smartphones still represent a small fraction of a complex multi-sensor platform enabling the passive collection of users’ travel behavior. Smartphone-based travel survey data yields the richest perspective on the study of inter- and intrauser behavioral variations. Yet after over a decade of research and field experimentation on such surveys, and despite a consensus in transportation research as to their potential, smartphone-based travel surveys are seldom used on a large scale. Purpose This literature review pinpoints and examines the problems limiting prior research, and exposes drivers to select and rank machine-learning algorithms used for data processing in smartphone-based surveys. Conclusion Our findings show the main physical limitations from a device perspective; the methodological framework deployed for the automatic generation of travel-diaries, from the application perspective; and the relationship among user interaction, methods, and data, from the ground truth perspective.

Travellers’ preferences towards existing and emerging means of first/last mile transport: a case study for the Almere centrum railway station in the Netherlands

First/last mile transport is essential for transit but is often found to be the weakest link in a trip. Moreover, as a result of multiple developments (e.g. demographic shifts, urbanization, climate change, technology advancement) first/last mile transport will likely change rapidly. The literature review of this study shows six different categories of factors affecting first/last mile mode choice: (1) traveller, (2) psychological, (3) first/last mile trip, (4) first/last mile modes, (5) built environment, and (6) main stage. We used this framework to understand and predict the complex process of mode choice, specifically given the emerge of new modes. The performed mode choice experiment shows varying results regarding the preferences of travellers for existing and new means of first/last mile transport. Four future scenarios (varying in level of sharing and flexibility of rides) are investigated. Traditional means of transport such as private vehicles and traditional ride services remain preferred over shared vehicles and on-demand ride services. For instance, 21% of the travellers chooses a private but no shared vehicle, and 12% chooses a traditional but no on-demand ride service. On the other hand, 21% of the travellers prefer a shared vehicle and 23% prefer an on-demand ride service whenever these vehicles/services are available. These results illustrate that when mode choice factors are absent and there are no restrictions taken into account (for example the possession of a car and driving license when choosing car), the actual chosen means of transport in the current situation differs from the preferred means of transport in the future. The results also show potential for new, emerging, means of first/last mile transport. According to the ‘preferred situation’ by travellers, transit nodes and first/last mile systems require a different design regarding first/last mile facilities, dependent on the scenario(s) that will develop. The challenge for decision makers and planners is to steer mode choice decisions in the direction according to their policy objectives, where our insights support the corresponding design choices and policy interventions.