Effect of Self-driving Buses on Vehicle Scheduling

Willingness to ride and perceptions of autonomous public transit

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

Based on BPS data in 2010, the number of motor vehicles in Indonesia as many as 76.9 million units, the number of vehicles on the island of Java alone approximately 44.4 million units. More than 57% of the number of motor vehicles in Indonesia on the island of Java. This is the background to the potential conversion of fuel oil to CNG fuel for vehicles on the island of Java. In addition, the end of 2014 the gas pipeline Trans Java linking West Java to Central Java and Central Java to East Java will operate. These two components, namely the large number of vehicles and the operation of the gas pipeline Trans Java offer more value than if the conversion program implemented in other islands. Studies have been done on the conversion of fuel oil to CNG fuel vehicles, especially in the area around the gas pipeline Trans Java. This paper will map potential locations for the implementation of the conversion program, conversion targets in each region, the need of CNG, Gas Stations number, size reduction of subsidies, and profits for NGV users. Formulation of strategies are required in migrating to CNG, the mapping problem in doing the conversion, and prepare for technology transfer. Vehicles that will be converted are gasoline-fueled vehicles, consists of public transportation, private and services. For public transport and services. Conversion is done 100%. Non public transportation (vehicles) converted only by 10%. The conversion vehicles will be done for 3 years gradually. Target conversion for 3 years at 1.1 million units NGV. The amount of the required gas allocation as many as 10.7 million klsp, which is needed as many as 2,857 SPBG. Saving from subsidy about 35.44 trillion rupiahs. To make the program successful requires the government policy and good coordination between relevant agencies in particular to overcome obstacles, problems, and to prepare for the transfer of technology.

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

Applications that use communication networks and distributed systems to control traffic have high latency, especially in critical situations. The performance of these applications largely depends on the computational delay of algorithms that run on local or central processors. Therefore, providing an optimized solution to minimize this delay to a tolerable range is highly needed. This article studies a method in which autonomous vehicles around an intersection try to control the intersection traffic efficiently by communicating and interacting with each other and road-side smart devices. This problem can be addressed in the form of a network utility maximization problem. To achieve a solution that is close to an optimal solution, a gradient descent algorithm with a fixed step size can be utilized. It is necessary to find a balance between latency and accuracy, which leads to finding a velocity close to the optimal velocity. The number of loop repetitions in the scheduling algorithm, determines the latency in preparation for making the proper schedule for autonomous vehicles. In this work, we propose an approach to provide an optimized schedule for autonomous vehicles in intersections considering pedestrian traffic. Autonomous vehicles are able to communicate with each other and road side unites. However, surveillance cameras are required to observe pedestrians passing the intersection. Hence, we utilize cameras, smart sensors, processors, and communication equipment embedded in autonomous vehicles and road side unites, to collect the required data, process it, and distribute the calculated optimal decision to autonomous vehicles. To simulate the traffic behaviors resulting from applying the proposed solution, Simulation of Urban Mobility software is used.

In this paper there are proposed methods and algorithms supporting city traffic controllers in effective perception and analysis of the visual information from the public transport monitoring system implemented in the City of Warsaw. To achieve this goal, public transport vehicles must be recognised and tracked in camera view. In this work, we describe a structure and give preliminary results for the detection and tracking system proposed. The algorithms discussed in this paper uses background subtraction to extract moving vehicles from the scene and the classification system to reject objects that are not city buses. Furthermore, a custom tracking module is utilized to enable labeling of city buses instances. During the test performed in the City of Warsaw the system was able to successfully detect 89% bus instances giving less than 15% erroneous detections.

In order to improve the capacity of transportation vehicles scheduling in complex geographical structure cities, an optimized scheduling algorithm for transportation vehicles in complex geographical structure cities based on improved multi-layer coding genetic algorithm is proposed. The map grid model of urban transportation vehicle route planning with complex geographical structure is constructed, and the clustering between urban road traffic target points and information difference degree are used for fusion clustering analysis, so as to construct an artificial routing optimization control model of urban road traffic planning vehicles, and realize the update of the optimal scheduling of urban road traffic planning vehicles by combining the reference factors of environment, map and pheromone. Get the instance initialization parameter distribution model of urban road traffic planning vehicles, optimize the spatial configuration parameters of urban traffic vehicles with complex geographical structure, build a hybrid improved multi-layer coding genetic evolution optimization model for optimal scheduling of urban traffic vehicles with complex geographical structure, and build an artificial intelligence algorithm for optimal scheduling of urban traffic vehicles with complex geographical structure according to the hybrid improved multi-layer coding genetic path constraint optimization method. The simulation results show that this method has better optimization ability and stronger path planning ability for urban transportation vehicle scheduling with complex geographical structure, and improves the efficiency of vehicle scheduling.

This paper examines time windows for scheduled trips in multiple depot vehicle and crew scheduling problems that arise in public bus transportation. In practice, the two planning tasks vehicle scheduling and crew scheduling are traditionally solved sequentially with the implicit understanding that the scheduled time for timetabled trips remains fixed. In order to improve cost efficiency two concepts have been developed over the last years: In order to obtain better flexibility when scheduling crews, vehicle and crew scheduling problems are tackled simultaneously. In order to extend flexibility while scheduling vehicles, variable trip departure and arrival times are considered. Obviously the combination of both concepts promises the largest savings, but probably leads to bursting computational times due to growing problem complexity. In this paper we combine both concepts by extending the integrated vehicle and crew scheduling problem with the possibility to shift scheduled trips within defined time windows. We examine the tradeoffs between solution quality and computational time for different solution approaches.

Transport activities are a significant factor in environmental pollution, especially in cities. Therefore, measures aimed at electrification of public transport are particularly important. The aim of the paper is to present the origins, status and development dynamics of electromobility in Polish cities, especially the second generation of electromobility, i.e. vehicles that do not require continuous connection to the energy source. In practice the second-generation electric vehicles can be identified with battery-powered vehicles, hydrogen and hybrid vehicles. The study was prepared on the basis of an analysis of literature, industry documents or development strategies. In addition, a database of information on zero- and low-emission vehicles in public transport (i.e. electric and hybrid buses) was compiled to analyse the phenomenon. The study shows that the implementation of electromobility in Poland has already emerged from the initial phase. The possibilities for developing battery technology vary in cities of different sizes. In 2021 in Poland, the share of low-emission buses in the public transport fleet was several times higher than that of electric vehicles among passenger vehicles. It is most likely that the Polish road to electromobility leads primarily through public transport. The following factors influencing the development of electromobility were identified: these were primarily EU and Polish legislation and regulations, the presence of manufacturers of rolling stock and electrotechnical equipment, and – at the local scale – organisational, economic and social issues.

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.

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.

Innovative technologies such as autonomous and connected vehicles can create a better transit mall, a street where only public transit vehicles, bicycles, and pedestrians are allowed. However, successful introduction of such technologies depends on user acceptance. To assess the potential acceptance of these technologies in the context of a new transit mall environment, it is crucial to provide an opportunity to experience them before they roll out. This study explores how real and video-based experiences of new technologies can affect public acceptance of new transit mall options in Hiroshima City, Japan, where trams and buses are significant modes of public transportation. We conducted a survey on the stated acceptance of new transit mall options with three groups of respondents: real experience, video-based experience, and no experience groups. The real-experience group self-selected themselves for the experiment, which potentially caused bias. Thus, we used a hybrid choice model with attitudinal variables (ethical, legal, and social concerns regarding emerging technologies, as well as risk-related factors) introduced to control for this bias. We found that both real and video-based experiences improved overall acceptance of new transit mall options, but there were differences in preferences between those who had only video-based experiences and those who had real experiences. We also found that prior knowledge of new transport services and infrastructure had a considerable influence on stated acceptance.

Bottom-up social innovations are being used in the transportation sector with unintended outcomes for carbon emission reducing potentials. Activists and policy makers have called for studies on the potentials of such innovations in reducing carbon emissions to aid policy recommendations. This study explores the potentials of such an innovation in the transport sector in Nigeria of reducing carbon emissions. Called Message by users, this social innovation is used like an informal courier service to deliver items of trade, documents and medicine among others using the public transportation system. This social innovation saves users costs of transporting the items themselves using public or private transportation or formal courier services. Data on number of Messages sent was collected by personal observation at a public transport terminal for Ikeja bound passengers from Ile-Ife (towns in southwestern Nigeria) for seven days. Ninety-four Messages consisting of 106 items were observed to be sent at the terminal using the Message system during the period. Two scenarios that may lead to savings in carbon emissions were highlighted in the study; the first, if the sender boards a public transportation vehicle to deliver the item and the second, if private transportation was used rather than the Message system. The round-trip distance for the two options was estimated to be 400 Km. A six-passenger 2-litre engine public transportation vehicle which consumes an estimated 9L/100 Km was assumed for the study. The results showed that for the first scenario, 1,297.2 Kg CO2 will be produced and 7783.2 Kg CO2 for the second scenario if senders had used these alternatives other than the message system for the 94 Messages. The study concludes that the Message can be regarded as a Low-carbon Bottom-up social innovation and recommends public and/or private efforts to scale-up this innovation in other climes.

The deployment of autonomous vehicles (AVs) is an exciting goal for many cities, as evidenced by the number of AV technologies that are currently on trial around the world, such as in The Netherlands, the United States, the United Kingdom, Sweden, Germany, and Japan. Examples of AV trials include autonomous trucks for garbage collection, autonomous freight vehicles for moving goods across cities, and autonomous buses and shuttles for public transport. The rapid development of AV technologies in recent times brings us closer to a full deployment of AVs in many cities.

Multiple depot vehicle scheduling with controlled trip shifting