Optimization Days 2024

HEC Montréal, Québec, Canada, 6 — 8 May 2024

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TB4 - Transportation

May 7, 2024 01:30 PM – 03:10 PM

Location: Lise Birikundavyi - Lionel Rey (blue)

Chaired by Ismail Sevim

3 Presentations

  • 01:30 PM - 01:55 PM

    Potential use of electrical trucks in forest transportation

    • Mikael Rönnqvist, presenter, Université Laval

    Fossil free forestry transports are important to reach climate goals. In Sweden, road transports account for around 50% of the industry’s CO2 emissions and almost 20% of the road freight volumes. Previous studies have shown that electrification is a cost-effective way for carbon abatement, while at the same time the requirements for flexibility in routing makes electrification of forestry transport challenging.

    We have developed an analysis tool that computes different energy consumptions for different road profiles when comparing diesel and electrical trucks. This is then used in detailed route optimization for a mixed fleet. A case study with 48 trucks, 14 industries and 219 harvest areas are used for what-if scenarios. This is used to answer questions like: Which flows are cost-effective to electrify? What share of the fleet can/should be electric? How does electrification affect the logistics structure and flows? Moreover, a sensitivity analysis is performed, to explore which parameters have the largest influence.

    Preliminary results indicate that electrified timber trucks can be employed at cost parity with diesel trucks. In a mixed fleet, it is optimal to plan for high utilization rate for the electric trucks to compensate for their higher purchase price.

  • 01:55 PM - 02:20 PM

    Public Perception and Acceptance of Automated Shuttles for Last-Mile Connectivity in Montreal

    • Anjali Awasthi, Professor, Concordia University
    • Rubel Chandra Kar, presenter, MSc Student, Concordia University

    This thesis investigates the public perception and acceptance of automated shuttle services for last-mile connectivity in Montreal. Through a comprehensive survey, the study examines key factors influencing acceptance of the autonomous shuttle, including experience, awareness, comfort and safety level, trust in technology, benefits and barriers, and potential integration into urban transportation systems. A survey of Montreal residents (n=52) reveals key insights into demographic trends and attitudes towards autonomous vehicles (AVs). Results indicate a moderate familiarity with AVs (38.6%) compared to the US (70.90%), UK (66%), and Australia (61%). Despite this, Montrealers expressed positive sentiments towards AVs (54%), slightly higher than the UK and US. Concerns about safety (49% very concerned), legal liability (47.10% very concerned), and data privacy (63.50% very concerned) were prominent. Comfort levels with autonomous technology varied, with 38.45% having heard of autonomous shuttles but only 13.46% having boarded one. Respondents showed preference for level 3 automation (56%) over higher levels. Concerns about interactions with other vehicles, pedestrians, and bikers were noted. Overall, Montreal residents are open to AVs but harbor significant concerns, highlighting the need for targeted interventions to address safety, security, and privacy issues in deploying automated shuttle services effectively.

  • 02:20 PM - 02:45 PM

    A Constrained Traffic Assignment Problem Model for Congestion Analysis of Electric Vehicle Fast Chargers

    • Ismail Sevim, presenter, Universite de Montreal
    • Margarida Carvalho, Université de Montréal
    • Ribal Atallah, Hydro-Québec

    In this study, we model the congestion in the electric vehicle (EV) fast chargers as a traffic assignment problem (TAP) with recharging constraints. The model aims to find the most congested stations and to further analyze them to increase the service level. We also work on a gradient projection algorithm implementation for the problem. As the case study, we analyze the Province of Quebec, Canada in a collaboration with Hydro-Quebec, the administration responsible for establishing and maintaining EV charging stations. Preliminary computational results revealed that the proposed model can estimate the congestion in the stations accurately.

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