ITS Monday: Edition 18, 2024

MaaS needs to become ‘Mobility as a Feature’, says transport academic

A recap of comments from Professor John Nelson from the Institute of Transport and Logistics Studies comments at a event. “Future success may depend on reimagining a ‘Mobility as a Feature’ mindset where transport services are delivered within a broader ecosystem of sectors that interact with mobility, such as real estate, banking or telecommunications.”

Related iMOVE articles:

• MaaS (Mobility as a Service) in Australia
  
• MaaS: What have we learned, and where to next?

Related iMOVE project:

• ODIN PASS: A Mobility as Service trial at UQ

Mobility-as-a-Service and Travel Behaviour Change: How multimodal bundles reshape our travel choices

More MaaS, this time a new paper by Professor David Hensher. The abstract:

MaaS offers an appealing narrative around sustainable mobility as being a useful tool for the transportation sector to achieve sustainable goals by changing users travel behaviour. Empirical evidence on how MaaS may lead the transport sector to a more sustainable future is rare due to the very limited number of real-world trials and commercial offers of MaaS.

Leveraging high quality data collected from the Sydney MaaS trial and the Application of Programming Interface (API), this paper develops an original mode choice model that describes the choice of transport mode under the MaaS era. Results offers the first insight into how multimodal bundles reshape mode choices and the extent to which subscription bundles could be used as a powerful tool to nudge users towards more sustainable choices. \

The results show that multimodal bundles present an appealing alternative for the users and help them reduce their private car use. Simulations were conducted to provide guidance for designing multimodal bundles that are both attractive to users and friendly to the environment.

Read more of Processor Hensher’s thoughts on transport through articles he has written for iMOVE.

Transport and Infrastructure Net Zero Consultation Roadmap

The Australian Government has produced a draft version of a Transport and Infrastructure Net Zero Consultation Roadmap, on which it is inviting feedback. The roadmap document is downloadable at the link, as is the opportunity to provide feedback.

Related iMOVE articles:

• Prospects for decarbonising freight transport in Australia
• FACTS: A Framework for an Australian Clean Transport Strategy

Related iMOVEprojects:

• Environmental impacts of Connected and Automated Vehicles
• EV batteries: Repair, refurbish, repurpose, recycle
• Micromobility and freight: Exploring opportunities in WA
• OneDock: Supercharging e-micromobility
• Consumer adoption of technologies for Net Zero emissions

The ellinikon takes shape — what we know so far about Greece’s 15-minute smart city

“The €8 billion masterplan at Ellinikon is making headlines as one of Europe’s largest urban regeneration projects currently underway in Athens, Greece. Rooted in the principles of a ’15-minute city’.”

Related iMOVE article:

• The 30-minute city: Small decisions for big gains
• Zoe Eather: Smart community advocate

Aurora and Volvo unveil self-driving truck designed for a driverless future

“A new self-driving truck — manufactured by Volvo and loaded with autonomous vehicle tech developed by Aurora Innovation — could be on (USA) public highways as early as this summer. ”

Related iMOVE articles:

• Autonomous Driving Info, Projects & Resources
• Autonomous Driving Technology
• Tim Camilleri: Prime mover in new truck tech

Stochastic Switching Mode Model based Filters for urban arterial traffic estimation from multi-source data

To close this week, two new academic papers. The abstract of this, the first of the two:

There has been extensive research in traffic state estimation that accounts for the stochastic nature of traffic flow models. However, these studies often exhibit limitations such as an exclusive focus on motorway traffic and a reliance on a single data source. This paper departs from these methods by introducing a stochastic estimation framework that is specifically designed for urban arterial traffic.

The framework has the capability to incorporate multiple data sources, which serves to improve its accuracy and robustness. The framework is composed of three components: (i) a stochastic traffic flow model, (ii) a filtering algorithm, and (iii) an algorithm for incorporating multi-source measurements. In terms of the traffic model, we introduce a new stochastic Switching Mode Model that can be applied to arterial roads that have both signalized and unsignalized intersections. This model does not consider uncertainty in the current mode of operation, which substantially reduces the computational complexity because there is only one mode at each time step.

Furthermore, we propose three different filtering algorithms for multi-source traffic estimation, including the incremental stochastic Kalman Filter (SKF), the incremental stochastic Unscented Kalman Filter (SUKF), and the hybrid approach. Since the SKF can only deal with linear functions, non-linear measurement equations need to be linearized using first-order Taylor expansions. The SUKF is based on the Unscented Transform (UT), which enables it to work with a wider range of functions regardless of linearity, non-linearity, or non-differentiability. The hybrid algorithm is a combination of the SKF and the SUKF, in which linear equations are treated similarly to the SKF, and non-linear equations are handled with the UT in the same way as in the SUKF. The performances of these algorithms were similar when applied to the synthetic data of an urban arterial in Christchurch CBD, New Zealand. The hybrid algorithm, however, worked slightly better and was more stable than the other two.

Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Academic paper #2. The abstract:

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity.

Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes.

The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM).

Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes.

This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety.

Related iMOVE article:

• Road Safety: Info, Projects & Resources