Autonomous Bus Research Articles

Exploring autonomous bus users’ intention: Evidence from positive and negative effects

The introduction of autonomous buses (ABs) in many countries addresses traditional bus operation challenges, but overlooks critical perceived risks, like functional, safety, and security issues, affecting autonomous driving technology acceptance. This research uses the mental accounting theory to investigate both the positive and negative effects of the intention to use ABs. A hybrid discrete choice model captures latent and observed factors and considers COVID-19's impact on travel mode preferences. Results show that both latent and observed factors significantly affect AB adoption intention, including compatibility, relative advantage, safety/security risks, perceived risks, in/out-of-vehicle time, and travel cost. Trial rides positively impact adoption intention. Familiarity with unmanned tech also affects choice of shared bicycles and ABs, allowing for tailored promotional programs. The conclusions herein offer insights for bus operators, manufacturers, and city governments to integrate ABs into public transport systems effectively.

On demand ride sharing: Scheduling of an autonomous bus fleet for last mile travel

Autonomous buses are expected to expand the mobility-on-demand options in cities in the next 10 years. An essential aspect of ensuring optimal usage of fleets of autonomous buses is the task of scheduling. This paper presents a scheduling approach based on the construction of all possible trips used to formulate an optimization problem. While in the past most research is focused on the scheduling of taxi trips, there is an increasing interest in the research for the scheduling of last-mile travel options. A street network has been generated based on open street map data as a basis for the scheduling. All possible combinations of buses and requests are calculated, and for each of those trips, a close to optimal order of requests is created. These are used to formulate the optimization problem, calculating a close to optimal assignment of requests on the buses. The approach has considered constraints such as maximum waiting time, travel delay, and targets to exploit shared trips for higher efficiency. Experiments have been carried out in a simulated environment of a university campus area with fleets of up to 10 vehicles. By performing various trials with changing parameters, the influence of the constraints on waiting time and travel delay to the scheduling is determined. Depending on the setup, service rates above 90%, while trails with strict constraints show that the approach can handle short-term requests. Based on the results, a use case-specific composition of the autonomous bus fleet can be done.

All Hail!

After years of planning, the UK’s first fully autonomous bus service has launched in Scotland. It’s being billed as the world’s most ambitious and complex autonomous bus pilot, and could pave the way towards safer, more reliable, efficient and accessible public transportation. Lauren Dyson talks to the experts overseeing critical testing

Bit scanner: Anomaly detection for in-vehicle CAN bus using binary sequence whitelisting

Due to the vulnerability of in-vehicle networks, particularly the Controller Area Network (CAN), malicious remote intrusion into vehicles has been a prominent public concern in recent years. The majority of existing technologies disregard the importance of the frame payload, making it difficult to detect tampered frames. In this paper, we propose Bit Scanner, a whitelist-based anomaly detection method that employs fine-grained checking for frame payload. Bit Scanner has no dependency on the CAN periodicity or CAN protocol. Its detection capabilities are evaluated using genuine CAN traffic collected from an autonomous bus. The results show that the proposed method has a malicious frames detection rate improvement of about 20% under replay attack scenarios, and a malicious packets detection rate improvement of at least 20% under tampering attack situations compared to the state-of-art methods.

Evaluation of risk injury in pedestrians’ head and chest region during collision with an autonomous bus

Autonomous vehicle (AV) can detect pedestrians with high accuracy by utilising various sensors. Although it is an undesirable condition, traffic crashes can occur if any sensor misfunctions. This paper focuses to measure the severity of pedestrian injury during autonomous bus-pedestrian crashes via finite element simulation as pedestrians are very vulnerable in traffic crashes. Two types of vehicle impact were considered (frontal and side), and risk injury in head and chest region were evaluated. Effect of vehicle velocity (ranging from 5–10 m/s) on risk probability were evaluated. It was found that higher velocity caused more damage to pedestrian body, and frontal impact was more severe than the side impact. Maximum values of head-injury-criteria and chest acceleration were 248 and 36 g, respectively, when vehicle speed was 10 m/s. AV developers can use these results to implement a safer AV design and reduce pedestrian risk injury.

Autonomous shuttles contribution to independent mobility for children – a qualitative pilot study

Children's mobility and the opportunity to get around the city on their own has decreased and is today limited to a large extent by unsafe traffic situations. Due to their limited independent mobility, children are excluded from the urban space and its facilities. Independent mobility can be related to several dimensions of children's social, physical, mental and cognitive wellbeing. The purpose of this study is to investigate if and in what way autonomous buses can contribute to children's independent mobility and, using children's experiences, provide insights into this before the introduction of autonomous buses in cities and public transport systems. To do so, the two research questions "How can autonomous buses in the transport system contribute to children's independent mobility?" and "What prerequisites are necessary for autonomous buses to contribute to children's independent mobility?" are answered. The survey is based on a case of introduction of autonomous buses in urban environments. Three focus groups with students in a primary school were conducted with the aim of gaining the participants' experiences. The results indicate that the introduction of autonomous buses in an urban area can contribute to children's independent mobility in several ways and that the buses have certain characteristics that enable them to be adapted to children's conditions. The low speed of the shuttles, which is often seen as an obstacle when it comes to time efficiency, has a positive effect on perceived safety in this case. The mobility solution is also child-friendly in that it is accessible in terms of trip planning and payment. However, it is important to consider the risk that the bus might replace active transportation modes, like cycling. To avoid this, autonomous shuttles should be integrated in a way that complement, not compete with, active travel.

A world of fully autonomous mobility options: On long-distance travel mode choice

What might long-distance travel behaviour look like in a world of fully autonomous mobility options? Within this context, which of the transportation options, including autonomous cars, pilotless planes, driverless trains, and driverless buses would people choose for their long-distance travel? We examine the extent to which people's attitudes towards fully autonomous mobility options, habits, and the level of satisfaction with current travel modes, as well as individual and socioeconomic variables, influence the choice/preference for fully autonomous modes. Data for this study were collected through an online survey (n = 811). A choice model was used to analyse the effects of attitudinal factors and other variables on the probability of choosing four fully autonomous alternatives. The results of the model show that (1) the preferred order of autonomous mobility options is: Autonomous Car (AC), Autonomous Train (AT), Autonomous Flight (AF), and Autonomous Bus (AB), (2) AT was evaluated the safest, most relaxing, and most economical alternative, and (3) both attitudes and habit influence the choice of fully autonomous vehicles. However, sensitivity analyses show that attitudes will have a greater impact on travel behaviour compared to habits. Policy-wise, a demographically differentiated campaign for changing attitudes can be employed.

Agent-based models for simulating e-scooter sharing services: A review and a qualitative assessment

E-scooter sharing services have grown exponentially in many cities of the world within the last 10 years, mainly with the goal to serve first/last mile trips. Compared to other shared mobility modes (e.g., autonomous buses and electric taxis), for which Agent-based Models (ABMs) have been applied in many cases, just a few studies attempted to simulate e-scooter trips. This study aims to bridge the gap between ABMs and e-scooter sharing services by reviewing the existing ABMs and conducting a qualitative assessment. Initially, existing ABMs are described based on ten descriptors. To test suitability of each model for simulating e-scooter sharing services, we developed an evaluation checklist based on empirical findings. The ten criteria refer to the capabilities of each model to (a) adjust in new challenges via an open-source code, (b) model shared mobility modes, (c) perform large scale simulation, (d) describe spatiotemporal variation of demand, (e) simulate bicycle, (f) pedestrian, and (g) mixed traffic (h) consider socio-demographic characteristics, (i) integrate new choice models, and (j) model multimodal trips. Our results reveal the advantages and disadvantages of each model in simulating flexible transport modes and services. We end up with a dilemma or a scalability problem: to model e-scooter riding behavior in link level or e-scooter services in network level. It is concluded that the dual behavior of e-scooter users (pedestrian or vehicle) poses new challenges that can be met through the development of new extensions or hybrid simulation models.

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies

Objective The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomous driving technology. Methods For the purpose of risk identification, the actual operation data for autonomous buses in Shanghai were converted into 3 kinds of grayscale images and 1 kind of radar image from a temporal–spatial perspective, and a deep learning convolutional neural network, AlexNet, was applied for image recognition. This article uses several image data augmentation strategies to address the problem of uneven distribution of samples and compares the effectiveness of different strategies. Results The optimal accuracy (ACC) of the risk identification was 90.4%, the optimal true positive rate (TPR) was 83.7%, and the optimal false negative rate (FPR) was 94.58%. The accuracy of risk identification using AlexNet was higher based on the sample containing Fourier images. In addition, risk identification accuracy based on grayscale images was higher than that based on radar images. Conclusions Autonomous buses were found to be vulnerable to risks in areas such as turning sections and intersections. In addition, the results show that the Fourier transform, an image augmentation strategy, can effectively solve the uneven sample distribution problem, and the length of the input time series has an impact on the accuracy of the risk identification.

Interaction of public autonomous vehicles with stochastic driving modes on Indian roads

Autonomous vehicles (AVs) have advanced greatly due to research during previous decade. They can affect mobility and traffic dynamics. Implementing AVs as public transport is a rising subject of study because it's projected to improve network efficiency, safety, and congestion. Previous studies lack focus on heterogenous conditions during the transition phase especially in developing countries like India. This paper studies the transition phase on a real-world Indian road network by introducing Autonomous Buses (ABs) at varied penetration rates using a microsimulation platform. Results indicate that the network improves once 60% ABs are plying on the network. Compared to a bus-only lane, the network functions better without ABs, providing useful insights into their practicality. The study also suggests upgrading future microsimulation platforms at each stage.

Comparing the performance of lateral control algorithms on long rigid vehicles in urban environments

Existing research on autonomous vehicle control is largely focused on how control algorithms perform on cars; long vehicles or buses have not been significantly investigated. Existing studies indicate that people display positive attitudes to using autonomous buses, indicating the value of researching control algorithms for autonomous buses. To provide insight on the performance of control algorithms on autonomous buses, we compared multiple lateral control algorithms on how well they maneuver a long vehicle around three courses resembling urban environments. We compared the Stanley and pure pursuit control algorithms and two new control algorithms which were improved versions of the Stanley and pure pursuit controllers. We compared the control algorithms in a kinematic simulation which recorded the steering angle and cross track error from the front axle for each controller driving the vehicle at 50 km/h around the course. We hypothesized that the Stanley control algorithm would perform the best due to its success in navigating existing vehicles. The Stanley control algorithm had a low cross track error, but it had a large steering angle and large changes in steering angle. The pure pursuit controller had smoother changes in steering angle, but the cross track error was larger than the Stanley controller. Our results suggest that none of the algorithms we tested were optimal, and that an algorithm which can utilize the ability of the Stanley controller to maintain a low cross track error while also keeping a low steering angle change will perform the best for long vehicles.

Benefits and Challenges of Integration of High-Capacity Autonomous Buses to Public Transport Operations

High-capacity autonomous buses can make bus travel safer, smoother, and more energy efficient. However, despite clear societal and environmental benefits there are still several challenges to be addressed. In this article we present the outcomes of two research projects focusing on the public acceptance of high-capacity autonomous buses. Results indicate high degree of trust towards automation safety. However, in certain conditions, such as driving alone at night, perceived security appears to be an issue. In such circumstances digital forms of assistance replacing drivers will most likely not guarantee a sufficient degree of security. The paper also argues that bus automation is not a silver bullet to make PT more attractive. Instead, it shall be seen as rather an enabler of new applications allowing to efficiently prioritize PT in traffic flows to make it an attractive choice.

Robust Sliding Mode Control With Optimal Path Following for Lateral Motion of Autonomous Bus

Robust Sliding Mode Control With Optimal Path Following for Lateral Motion of Autonomous Bus

Energy management in DC microgrid with an efficient voltage compensation mechanism

Direct current (DC) microgrid facilitates the integration of renewable energy sources as a form of distributed generators (DGs), DC loads, and energy storage system (ESS) devices. A new voltage compensation mechanism is presented in this study to resolve the control issues of DC microgrid in a distributed manner. In this mechanism, a fractional-order voltage compensation term is used in the outer controller loop which eliminates the voltage deviation in the steady-state condition. A detailed mathematical model is developed for the ESS along with the new voltage compensation controller to facilitate proper tuning of the control parameters. Since the proposed voltage compensation term guarantees autonomous bus voltage restoration, the supercapacitor state of charge (SoC) remains at nominal value without violation while it only buffers fluctuating power. However, the battery only compensates for the nominal power demand. The DG power control algorithm strictly maintains the battery SoC within lower and upper bounds. A DC microgrid comprising hybrid ESS, DC load, constant power load (CPL), and distributed generator is implemented with real time digital simulator (RTDS). The results show that the proposed controller is reliable, leading to excellent ESS performance and power management within the microgrid, without any DC bus voltage deviation.

Autonomous bus services: current research status and future recommendations

Autonomous bus services: current research status and future recommendations

Analysing the acceptance of autonomous buses in real-life traffic environments: pilot project with tourists in Malaga, Spain

ABSTRACT Despite the significant technological progress with Autonomous Buses (ABs), there is still a limited understanding how users perceive and experience large, mass transit ABs in real-life traffic environments. This paper follows a pilot project on AB use by tourists in the city of Malaga, Spain. Discrete choice analysis was employed to study their willingness to use the AB and the passengers’ actual satisfaction on board the AB. The obtained results reveal higher willingness levels to adopt ABs among individuals more open to new technologies and more environmentally conscious. Higher levels of satisfaction on board are reported by older passengers, women, employed, private vehicle users, and people with environmental consciousness and openness to new technologies. Finally, the research discusses how socio-demographics and personality-related barriers affect the further implementation of autonomous mobility in public transportation systems, adding new reflections on the capacity of ABs to support or even replace conventional buses.

Moving toward a More Sustainable Autonomous Mobility, Case of Heterogeneity in Preferences

Autonomous vehicles (AVs) have a number of potential advantages, although some research indicates that this technology may increase dependence on private cars. An alternative approach to bringing such technology to market is through autonomous taxis (ATs) and buses, which can assist in making transportation more sustainable. This paper aims at examining the role of attitudinal, travel-related, and individual factors in preferences for a modal shift from conventional cars toward ATs and exclusive-lane autonomous buses (ELABs), exploring the existence of heterogeneity and its possible sources. The proposed mixed logit model with a decomposition of random coefficients uses 1251 valid responses from a stated preference survey distributed in Tehran, in 2019. Results show that there is significant taste variation among individuals with respect to ATs’ travel costs, ELABs’ travel times, and walking distances to ELAB stations. Furthermore, exploring the sources of heterogeneity indicates that women are more sensitive to ATs’ travel costs and walking distances to ELAB stations while they are less sensitive to ELABs’ travel times. Moreover, travel time in discretionary activities reduces the utility of ELABs more than it does in mandatory activities. Transportation authorities can use these findings to establish more effective policies for the successful implementation of AVs.

Explore public acceptance of autonomous buses: An integrated model of UTAUT, TTF and trust

Autonomous buses can improve the efficiency of public transportation and offer great benefits for developing sustainable mobility transportation. To realise the potential of autonomous buses in public transportation, understanding the public’s intention to use the new technology-based mobility concept is necessary. Therefore, this study synthesises three theories to explain public intention to use autonomous buses, namely, unified theory of acceptance and usage of technology (UTAUT), task technology fit (TTF) theory and trust theory. A survey instrument (n = 277) is adopted to validate the theoretical model. The results indicate that (1) performance expectancy (e.g., economic benefits, environmental friendliness and safety), effort expectancy (e.g., ease to use, availability and accessibility), social influence (e.g., attitudes of around people and information from society) and perceived risk (e.g., injuries, damages and privacy issues) lead to the formation of trust; and (2) trust has direct and indirect (via TTF) effects on behavioural intention to use autonomous buses. Furthermore, bootstrapping analysis is conducted, and the significance of the mediation effect of TTF is verified. The findings enrich the existing theoretical research on the adoption of autonomous buses and guide the future integration of autonomous buses into the public transportation system.

A Microsimulation Modelling Approach to Quantify Environmental Footprint of Autonomous Buses

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies.

Understanding Shared Autonomous Vehicle Preferences: A Comparison between Shuttles, Buses, Ridesharing and Taxis

Shared autonomous vehicles (AVs) will soon be introduced in public transportation as cities and their transportation systems become ‘smarter’. This brings long-term environmental, economic and societal benefits to cities. However, shared AVs will not only need to overcome technological challenges but also prevail against social barriers for successful marketplace penetration. Hence, we proposed and investigated the acceptance of four shared AV service designs for public use in this study, namely, autonomous buses, shuttles, AV rideshares and autonomous or robo-taxis. An online survey conducted in Singapore with 734 adults found the greatest receptiveness toward the introduction of autonomous shuttles, in part due to perceptions that they will perform well and be easy to adopt. This aligns with ongoing shared AV trials where AV shuttles are mostly used. Larger autonomous buses had the second-highest acceptance. AV rideshares and taxis seem to largely appeal to the existing regular users of the conventional counterparts of these services. These results suggest that to encourage a mode switch from public transport to ridesharing and taxis, or vice versa, shared AVs need to appeal to users beyond being an automated version of existing modes. That is, shared AVs need to address an underserved or unmet transportation need or population.