Impacts of autonomous on-demand mobility service: A simulation experiment in the City of Athens

High-capacity Connected and Autonomous Vehicles (CAVs) are expected to be extensively utilized by on-demand services. This paper aims to assess the impacts of large-scale autonomous on-demand mobility services on traffic, environment, and road safety, under various service specifications using microsimulation. To that end, an urban on-demand shuttle service was designed, optimized, based on a variation of the Dial-a-Ride optimization problem (DARP), and implemented in the road network of the city of Athens to serve different portions of demand with various capacity specifications. It was then investigated through forty mobility scenarios, with differences in policy implementation and market penetration rate of CAVs. Findings show that it led to improved network level traffic conditions, as delays decreased, and that traffic impacts evolve with fleet capacity and served demand. Furthermore, the number of conflicts decreased and the environmental conditions significantly improved, with CAVs in the network, while the traveled distance increased.

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Quantifying the implementation impacts of a point to point automated urban shuttle service in a large-scale network

Autonomous point to point shuttles are an emerging paradigm of a future mobility-on-demand ecosystem. However, the traffic and environmental impacts of their operation are largely under researched especially in relation to influential infrastructure related factors and service-related specifications.

The scope of this study is to reveal the factors that may affect the degree and magnitude of the road segment level impacts of an autonomous urban shuttle service (AUSS) operating in a city using microsimulation and structural equation modeling (SEM). For the purposes of this research, a systematic framework is developed and applied in the city center of Athens (Greece), which encompasses different scenarios of operations including: (i) Baseline (no AUSS operation), (ii) AUSS operation with a dedicated lane during peak hour, (iii) AUSS operation mixed with regular traffic during peak hour and (iv) AUSS operation mixed with regular traffic during off-peak hour. Two connected automated vehicle (CAV) profiles were used to model the advent of automation in the overall traffic: a cautious profile is introduced first, followed by a more aggressive profile. SEM findings indicate that the AUSS operation has a significant effect on cumulative travel time per segment and CO2 emissions per segment only during the scenario of mixed operation with traffic during off-peak hours. Additionally, the influence of the network geometry is correlated with reduced travel time and with increased CO2 emissions. Road traffic density was found to be positively correlated with both travel time and CO2 emissions, while the penetration of both cautious and aggressive CAVs was found to be negatively correlated with both indicators.

Read our publication ‘Quantifying the implementation impacts of a point to point automated urban shuttle service in a large-scale network’ based on the research carried out in WP5 in the Transport Policy Journal using this link.

Passenger Cars Microsimulation Sub-use Cases Findings

The Work Package 6 (WP6) of LEVITATE considers the specific case of passenger cars which are used across the transport system so forecasting of impact will consider the use on urban, rural and highway infrastructure. Work undertaken in WP6 is based on the methodology developed in WP3 and the scenarios developed in WP4 to identify and test specific scenarios regarding the impacts of CATS on passenger cars. Findings will complement those of WP5 (Urban transport) and WP7 (Freight transport) and feed into the developing of the LEVITATE Policy Support Tool (PST) in WP8. The aim of this WP6 is to forecast short-, medium- and, long-term impacts of automated passenger cars on safety, mobility, environment, economy and society. The objectives of the WP6 are set as follow:

  • To identify how each area of impact (safety, environment, economy and society) will be affected by the transition of passenger cars into connected and automated transport systems (CATS). Impacts on traffic will be considered cross-cutting across the other dimensions,
  • To assess the short-, medium- and long-term impacts, benefits and costs of cooperative and automated driving systems for passenger cars,
  • To test interactions of the examined impacts in passenger cars, and
  • To prioritise considerations for a public toolkit to help authority decisions.

According to Deliverable 3.1, a taxonomy of potential impacts of connected and automated transport systems (CATS) at different levels of implementation can be classified into three distinct categories: direct impacts refer to the operation of connected and automated transport systems by each user; systemic impacts are system-wide impacts on transport; and wider impacts are societal impacts resulting from changes in the transport system such as accessibility and cost of transport, and impacts like accidents and pollution and changes in land use and employment. In order to estimate and forecast these impacts, appropriate assessment methods have been proposed in LEVITATE such as traffic mesoscopic simulation, traffic microsimulation, system dynamics, Backcasting and Delphi panel method.

A stakeholder reference group workshop was conducted to gather views from city administrators and industry on the future of CATS and possible uses (i.e. use cases) of automated passenger cars, named, sub-use cases. Workshop participants suggested a few new use cases for passenger cars. Those include specific detailed parking related sub-use cases and in-vehicle signage. It was emphasised that in order to have a better future of AVs, parking issues would need to be solved. Within WP6, five sub-use cases have been defined as follows:

  • Road use pricing:
    1. Empty km pricing
    2. Static toll on all vehicles
    3. Dynamic toll on all vehicles
  • Automated ride sharing
  • Parking space regulation:
    1. Parking price
    2. Replace on-street parking with public space
    3. Replace on-street parking with driving lanes
    4. Replace on-street parking with pick-up/drop-off parking
  • Provision of dedicated lanes for AVs on urban highways, and
  • Green Light Optimal Speed Advisory (GLOSA).

This article will be focused on the initial findings by applying the traffic microsimulation for sub-use cases, specifically the initial findings of the provision of dedicated lanes for AVs on urban highways and parking price. It noted that all autonomous vehicles are electric and that they used two main driving profiles (Roussou et al., 2019):

  • Cautious: long clearance in car-following, long anticipation distance for lane selection, long clearance in gap acceptance in lane changing, limited overtaking, no cooperation, long gaps, and
  • Aggressive: short clearance in car-following, short anticipation distance for lane selection, short clearance in gap acceptance in lane changing, limited overtaking, no cooperation, small gaps.

Have a look at the whole article, written by Hua Sha (LOUGH), Hitesh Boghani (LOUGH), Amna Chaudhry (LOUGH), Mohammed Quddus (LOUGH), Andrew Morris (LOUGH), Pete Thomas (LOUGH).