Effects of Tram Warning Application on Automated Shuttle Bus and Tram Interaction

Urban transportation systems face difficulties due to the increase in urban population, as urbanisation is accompanied by higher levels of travel times, traffic congestion, delays and accidents (Goetz, 2019). These issues will be more prevalent in the future, since United Nations (2019) estimate that by 2050, 68% of the world’s population will live in urban areas. Additional statistics suggest that urban passenger transport is responsible for 40% of greenhouse gas emissions (GHG) in the total passenger transport sector related GHG emissions (International Transport Forum (ITF), 2021). The alarming issue of urban transportation is congestion; it is directly related to motorisation and the expanded usage of automobiles (Rodrigue, 2020). Congestion is followed by an increased need for transportation infrastructure and road space taken away by vehicle parking (Rodrigue, 2020). Automated shuttle buses could provide a solution to these issues related to individual transportationand improve effectiveness of public transportation. This paper presents a case study on an automated shuttle bus that shares a lane with a tram for part of its route. The research question is whether shuttle bus operation affects the tram’s travel time. Specifically, the influence of a traffic management intervention, such as a tram warning, on the travel time of both the automated shuttle bus and tram is investigated. The tram warning application is a new service that has been mainly tested on conventional vehicles. According to Zimmermann et al. (2021) tram warning application operates for two purposes: i. Application informs car drivers about an elevated risk of collisions with publictransportation,ii. Application also notifies drivers about the public transport existence at the stop. The recent study by Tarkiainen et al. (2024) tested the tram warning application in a real urban setting, in an automated shuttle bus testbed in Hervanta suburban area in Tampere, Finland. In the testbed, the automated shuttle bus shared a lane with a tram. Before activating the tram warning app, a slow-moving shuttle impeded the movement of a tram when operating ahead of it on a shared route. The shuttle increased the tram’s average travel time from 24 seconds (when the shuttle was not travelling in front of a tram) to 39 seconds (when it started operating). However, once the tram warning was activated, the instances of shuttle obstructing a tram decreased from every 10 days to every 25 days.Automated shuttle buses are intended to operate at higher automation levels, usually SAE Levels 4 or 5, where user’s assistance is not needed (Chaalal et al., 2023). They have the potential to enhance the attractiveness of public transportation for passengers and could function as connectors for public transport systems (Cao & (Avi) Ceder, 2019; Shen et al., 2018). Thus, evolving automated shuttle buses could improve public transport accessibility (Barillère-Scholz et al., 2020) and ensure uninterrupted last-mile connection (Huber et al.,2022). Additionally, automated shuttles can observe their environment since they are equipped with sensors and advanced communication logic (Chaalal et al., 2023). However,automated shuttle buses are still developing, often require human control and operate at a relatively low speed (Iclodean et al., 2020). In general, automated shuttle buses have limited passenger capacity and can transport up to 15 passengers (Fournier et al., 2023). However, a compact size makes them suitable for different services. Shuttles may operate on fixed routes or provide on-demand service, but they are most effective for scheduled services since they often function as feeders for the existing transport network (Soteropoulos et al., 2023). Automated shuttle bus operation has been tested across Europe and North America inmixed-traffic conditions. A pilot project from the Swiss city of Zug revealed that automated shuttle buses experience manoeuvring challenges, and their integration with public transport systems requires further technological advancements (Schweizerische Bundesbahnen (SBB), 2020). Trials in Baltic region cities showed that due to an obstacle detection issue, human drivers often took control of driving, even though shuttles were expected to operate at level 4 of automation (Bellone et al., 2021). Beauchamp et al. (2022) investigated automated shuttle operation in the Canadian urban traffic context. Recorded data revealed that shuttles travelled at relatively low speeds, 7-12 km/h, compared to motorised vehicles using the same path. Overall, case studies show the need for more research to integrate automated shuttles into public transport before increasing their use and automation. Like the above-described study by Tarkiainen et al. (2024), this paper presents a case study on the effectiveness of tram warning applications for an automated shuttle bus and tram interaction in the Hervanta testbed, but under different traffic conditions and in a simulated environment. The research findings have been published as part of a Master of Science thesis at the University of Helsinki, titled as Effects of an Automated Shuttle Bus on Urban Traffic in Tampere, Finland (Chkhartishvili, 2024).

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