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With rapid population growth and increasing demand for urban mobility, metropolitan areas such as Singapore, Tokyo, and Shanghai are increasingly dependent on public transport systems. Various strategies are proposed to improve the service quality and capacity of bus and subway systems. Express trains—i.e., trains that skip certain stations—are commonly used because they can travel at higher speeds, potentially reduce travel time, and serve more passengers. In this paper, we study cyclic express subway service (CESS), in which express trains provide routine transport service with cyclic (periodic) station-skip patterns that can be used in daily service. We propose an exact Mixed Integer Programming (MIP) model to optimize cyclic station-skip patterns for express trains operating in a single-track subway system. The objective is to reduce passengers' total travel time—i.e., the sum of waiting time and riding time—while considering demand intensity and distribution and train headway, frequency, and capacity. We implement the model in a set of numerical experiments using real data from Singapore. To solve the optimization problem more efficiently, we also propose a heuristic to solve large-scale problems. We observe that the exact MIP model for CESS provides optimal cyclic express service patterns within a reasonable computational time, and the heuristic method can significantly reduce the computational time and provide a good solution. The case study demonstrates that passengers' average travel time could be significantly reduced compared to local train service. We also discuss the potential transfer of passengers between express trains and evaluate its effects using numerical experiments. [ABSTRACT FROM AUTHOR]

The development of carsharing services is expected to achieve greater resource efficiency and provide a sustainable solution for future mobility systems. However, operators inevitably face the imbalance between demand and supply in one-way carsharing systems (CSSs). Also, it is challenging for them to make quick and efficient operational decisions when both travel time and trip requests are uncertain. This study leverages historical and online data and proposes a learning-based methodology to quickly make real-time decisions for CSSs, including vehicle assignment, relocation, and user incentive decisions. Compared to the literature in approximate dynamic programming (ADP), which mostly focuses on uncertainty in trip requests, we propose an offline–online ADP approach to consider the temporal and spatial uncertainty in both trip requests and travel time. To tackle our high-dimensional problem, we integrate an online look-ahead policy into the offline value function approximation (VFA) policy to produce a computational tractable high-quality dynamic fleet management policy. Furthermore, a user-based relocation strategy is investigated to rebalance the fleet distribution to meet the demand better. Specifically, we aim to solve the optimal incentives the operator could offer to users to relocate cars, while user preferences towards relocation incentives are generally unknown in practice. We further enhance our anticipatory policy by developing an online module via Bayesian learning that learns the preference model on the fly using users' revealed preference data collected online. The numerical experiments in Singapore demonstrate our offline–online ADP approach improves the solution quality and significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of the user-based relocation scheme using the Bayesian learning method using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. It quickly and efficiently learns the user preferences, which further reduces the relocation cost and increases the profit. • Consider both demand uncertainty and travel time uncertainty in one-way carsharing systems. • Investigate a user-based relocation strategy combined with staff-based relocation. • Propose an offline–online ADP solution framework to make real-time operational decisions quickly. • Learn users' willingness to relocate through Bayesian learning. • Demonstrate the performance of our solution framework under various scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of a driving simulator for ramp traffic control on expressways using a traffic simulator and control (TSC). The TSC consists of two main components: car-following model (CFM), and traffic controller (TC). The CFM simulates the car-following behavior and delivers aggregated traffic parameters to the TC to derive control actions. The CFM and TC are harmonized and integrated in a close-loop control manner, where the effects of the control by the TC are fed-back as inputs for the CFM in real-time applications. Although the following behavior of individual vehicles is simulated, the aggregated outputs such as average speed and flow rate from the model are the parameters of interest. For simplicity in the model development and validation and to capture lane-changing effects, the traffic in the multi-lane expressway where the data were obtained was equivalently represented as a single-lane system. The validation of the CFM was performed at macroscopic level where aggregated outputs from the model were compared to observed data in a segment of the Pan Island Expressway of Singapore under various traffic conditions. The result shows that the simulated speed is not significantly different from the actual speed at 5% significance level, and the aggregated flow rate discrepancies fall in a small range, from 2.21% to 3.15%. This shows that the TSC model is a reliable model for traffic simulation and control applications. • Traffic simulation models are useful tools in studying traffic systems. • Car-following model is key building block of microscopic traffic simulation models. • Validation is most important in developing microscopic traffic simulation models. • By studying statistical properties, CFMs explain behavior of macroscopic systems. • TSC model is a trustworthy simulator for traffic management and control. [ABSTRACT FROM AUTHOR]

For large-scale traffic assignment applications, mesoscopic simulation has more advantages than microscopic and macroscopic simulation because it has a well-designed tradeoff between the level of details and the running efficiency. Currently, the time complexity of time-stepped mesoscopic supply framework is linear to the number of vehicles on the lanes. To simulate an entire city the computation cost is often practically unacceptable. To tackle this problem, we propose a novel approach named 'Entry Time based Supply Framework (ETSF)' to improve the performance of mesoscopic applications. The main idea is that ETSF updates the 'entry_time_to_pass' and 'entry_time_to_queue' parameters of each lane to compute the moving/queuing parts of link/lane vehicles, which means reducing the time complexity of the number of vehicles passing the lanes. To validate this innovation of ETSF, a series of experiments were carried out on a sub-network of Singapore and the outcomes show that ETSF is less sensitive with the demand level (or the total number of vehicles) and greatly improves the performance, especially when simulating congested traffic scenarios. In the global research framework of mesoscopic traffic simulations, ETSF offers an innovative and time-efficient way to model traffic dynamics on links. [ABSTRACT FROM AUTHOR]

This study analysed the feasibility of a bike-sharing programme in Singapore based on a case study in the Jurong Lake district. Data were collected using a two-stage perception survey to gather the public's general awareness and opinions about bike sharing and followed by estimating the likely take-up rate of bike sharing. The results showed that bike-sharing schemes would be suitable around mass rapid transit stations and low rental costs would encourage bike sharing. Finally, a distance-based mode-choice model was established for four scenarios (morning peak and good weather, morning peak and bad weather, evening peak and good weather and evening peak and bad weather), focusing on walking, bike sharing and taking a bus. Weather could have an obvious impact on the frequency of bike sharing. [ABSTRACT FROM AUTHOR]

• A heterogeneous network with signalized and non-signalized intersections is modelled. • A traffic light control problem based on proposed macroscopic model is formulated. • The problem is converted to a mixed integer linear problem for computational benefit. • Simulations are shown to provide guidance for the design of an urban traffic system. In this paper, one type of heterogeneous traffic network, which consists of signalized intersections and non-signalized ones, is analyzed and modelled with a macroscopic model and logic constraints, which involves the evolution of the number of vehicles on each link and the leaving flow dynamics at each intersection. We propose a novel model describing the dynamic behaviors of such a system and the proposed model is validated via road tests in Singapore traffic network and simulations in VISSIM. A piecewise linear model is adopted to describe the traffic flow dynamics at each signalized intersection, while a platooning-like first-in-first-out (FIFO) model is proposed to depict the dynamics at each non-signalized intersection. A traffic light control problem for a heterogeneous traffic network is formulated based on the proposed macroscopic model as a mixed integer programming problem. Simulations indicate that the proposed optimal traffic control scheme could outperform the fixed-time traffic light control strategy and conventional actuated traffic light control scheme. Besides, we demonstrate potentials in developing a systematic planning approach on deciding which traffic intersections require signal control to ensure good traffic control performance. [ABSTRACT FROM AUTHOR]

Introduction: Road traffic accidents (RTAs) in Singapore involving children were evaluated, with particular focus on the epidemiology, surrounding circumstances and outcomes of these accidents. Key factors associated with worse prognosis were identified. We proposed some measures that may be implemented to reduce the frequency and severity of such accidents.Methods: This was a retrospective study of RTAs involving children aged 0-16 years who presented to the Children's Emergency at KK Women's and Children's Hospital, Singapore, from January 2011 to June 2014. Data was obtained from the National Trauma Registry and analysed in tiers based on the Injury Severity Score (ISS).Results: A total of 1,243 accidents were reviewed. RTA victims included motor vehicle passengers (60.4%), pedestrians (28.5%), cyclists (9.9%) and motorcycle pillion riders (1.2%). The disposition of emergency department (ED) patients was consistent with RTA severity. For serious RTAs, pedestrians accounted for 63.6% and 57.7% of Tier 1 (ISS > 15) and Tier 2 (ISS 9-15) presentations, respectively. Overall use of restraints was worryingly low (36.7%). Not restraining increased the risk of serious RTAs by 8.4 times. Young age, high ISS and low Glasgow Coma Scale score predicted a longer duration of intensive care unit stay.Conclusion: The importance of restraints for motor vehicle passengers or helmets for motorcycle pillion riders and cyclists in reducing morbidity requires emphasis. Suggestions for future prevention and intervention include road safety education, regulation of protective restraints, use of speed enforcement devices and creation of transport policies that minimise kerbside parking. [ABSTRACT FROM AUTHOR]

In this article, we present SUMMIT (Singapore Urban Multi-Modal Integrated Transport Simulator), a multi-modal agent-based simulation platform for public transport calibrated using real world mobility data sets from Singapore. SUMMIT uses a co-simulation approach in order to model multiple modes of public transport in tandem with commuters seamlessly transitioning between different mode simulations. This is implemented through a message passing framework codenamed Fabric (Fast, Agent-Based, Reproducible, Integrated Co-simulation) which helps to align and synchronise the simulation as it transitions through the different simulation time steps. Three stand-alone simulators, each modelling a key mode in the Singapore public transport system, namely train, bus and taxi, are integrated with a commuter control model, which simulates the commuter behaviour and takes care of commuters' transfer between different modes. We present one application of SUMMIT in public transport contingency planning, where train services are disrupted in multiple lines of Singapore Mass Rapid Transit (MRT) network. Different scenarios are simulated and key performance indicators are compared to help planners evaluate the efficacy of mitigation measures. The results show that bridging bus services, a commonly used mitigation measure, are effective in reducing crowd sizes within the train stations. However, we demonstrate that bridging buses could also result in worsening of commuters' travel time due to over-demand for these buses. Our results also show early dissemination of information to commuters during a train service disruption could reduce the negative impacts of the disruption event significantly. • City-scale agent-based simulation platform for multi-modal public transport. • Integrate train, bus and taxi simulation with commuter control model. • Analyse the mitigation strategies for train service disruptions in Singapore. • Bridging buses may not always help mitigate train disruptions. • Early announcement of train disruption helps reduce impact on commuter travel time. [ABSTRACT FROM AUTHOR]

This study is concerned with the optimal dynamical information dissemination (DID) problem in a transportation network interrupted by traffic incidents. Optimizing system performance with DID after road incidents is challenging because of the uncertainty in traffic flow variation and travelers' heterogeneous responses to information. To address the problem, we consider a traffic manager who aims to improve the system performance by dynamically generating and disseminating information to road users in a time period after an incident happens. We develop a decision tool for obtaining DID strategy based on double deep Q-learning (DDQL) for the traffic manager, aiming at finding an optimal DID strategy. The decision tool is integrated with traffic sensors which collect traffic data in real time. With advanced traveler information systems, the DID system dynamically sends out various types of information to users according to the current and anticipated traffic states so as to minimize congestion and enhance road network capacity. In particular, the proposed DDQL method utilizes a double deep Q-network (DQN) structure to learn the state–action values. To test and evaluate the performance of the decision tool, we develop a microscopic simulation model of a real road network in the Serangoon area of Singapore in PTV VISSIM and calibrate the model with real historical traffic data. We train and compare the DDQL controller model with different reward signals, including the weighted sum of the average speed and queue delay, total traffic flow, and average travel time. Numerical experiments demonstrate the good performance of the proposed DDQL-based DID approach in improving the congestion and other performance metrics of the expressway. The robustness and generalizability of the DDQL agent are also verified by evaluating the algorithm performance in environments with different traffic demand patterns and driving behavior profiles. • Develop a DDQL framework to generate optimal dynamic information dissemination policies. • Swiftly disseminate information to road users at multiple locations • Consider the uncertainty in travelers' aggregate diversion decisions. • Evaluate policy performance with different reward signals. • Demonstrate the performance of our solution framework by comparing it with various benchmarks. [ABSTRACT FROM AUTHOR]

Green transportation has been increasingly gaining attention in recent years. Existing pheromone-based traffic management frameworks were developed to reduce urban congestion by fusing traffic lights control strategies and vehicle routing schemes. Despite a significant reduction in traffic congestions, the greener aspects of transportation were not well investigated. In view of this, a Pheromone-based Green Transportation System (PGTS) is proposed to reduce Greenhouse Gas emissions and urban congestion in a three-step approach. First, traffic congestions are predicted based on the transport pheromone intensity of the target and adjacent upstream roads through an online epsilon-Support Vector Regression model. Second, a Coordinated Traffic Light Control (CTLC) strategy generates green wave scenario, dispersing heavy traffic on congested roads to the coordinated downstream paths. Third, a Cooperative Green Vehicle Routing (CGVR) takes a further leap by probabilistically rerouting upstream vehicles from entering the congested road, preventing the accumulation of vehicles that can lead to upstream congestion. Intuitively, the integration of CTLC and CGVR increases the chances that vehicles traversing multiple intersections with fewer frequencies of acceleration, effectively marking down fuel consumption. The proposed PGTS can be realized through a Pheromone-based Hierarchical Multi-Agent System (PHMAS). Based on Singapore traffic data, experimental results from a microscopic simulation SUMO show that the proposed PGTS outperforms other six approaches in reducing carbon dioxide emissions by 37.7%, fuel consumption by 37.6%, mean travel time by 47.5%, mean waiting time by 57.3%, and increasing number of arrived vehicles at designated destinations by 62.6%. • Green transportation was not well explored in existing pheromone framework. • Pheromone-based Green Transportation System (PGTS) is proposed to reduce emissions. • PGTS integrates coordinated traffic lights and cooperative green vehicle routing. • This integration disperses upstream and downstream traffic to reduce fuel burnt. • Vehicles can traverse intersections with fewer frequencies of acceleration in PGTS. [ABSTRACT FROM AUTHOR]

Singapore, facing a land scarcity issue, has been continuously looking for sustainable urban transport solutions to facilitate effective movement within the island nation. With the recent population projection of 6·9 million and the unprecedented national age shift (towards ageing) by 2030, investing more on non-motorised transport (NMT), namely walking and cycling, may be a workable solution. The first part of the paper focuses on assessing the first/last mile trips connecting to rail transit stations and evaluating NMT as a viable solution in the Singapore context. Engineering and behavioural studies aimed at providing adequate NMT infrastructure to the increasing demand of users are discussed next. Safety, accessibility and capacity of the NMT network are assessed by the use of the safety and accessibility index. Current key research findings (such as pedestrian shed, NMT push and pull factors and route choice models) are showcased. [ABSTRACT FROM AUTHOR]

Traffic incidents are a primary cause of traffic delays, which can cause severe economic losses. Effective traffic incident management requires integrating intelligent traffic systems, information dissemination, and the accurate prediction of incident duration. This study develops a clustering-based machine learning model to predict the incident duration. Unlike similar studies that train separate machine learning models for a fixed number of clusters, this study proposes an ensemble learning method based on multiple clustered individual models that can provide good and diverse prediction performance. The K-means clustering method is used in this study as a bootstrapping technique in the ensemble learning approach, with the individual models based on the artificial neural network model and random forest regression model. The models are tested using the incident data from Singapore, and the results show that the ensemble model outperforms both the traditional model with fixed clusters and the classical model without clustering. Additionally, this study attempted to determine the significance of different variables on traffic incident durations using the random forest feature importance function. The prediction of incident duration and the analysis of influence factors can contribute to several aspects of traffic management, such as improving traffic dissemination to mitigate traffic congestion caused by incidents. [ABSTRACT FROM AUTHOR]

Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve the accuracy of finding the real optimal path. By further adopting dynamic neural networks to learn the value function, our approach can scale well to large road networks with arbitrary deadlines. Moreover, our approach is flexible to implement in a time dependent manner, which further improves the performance of returned path. Experimental results on some road networks with real mobility data, such as Beijing, Munich and Singapore, demonstrate the significant advantages of the proposed approach over other methods. [ABSTRACT FROM AUTHOR]

An accurate short-term traffic forecasting model serves as an integral part to enhance the efficiency of vehicle rerouting and traffic light control strategies. The information exchange (pheromone) behavior of ants has been applied to forecast traffic conditions in existing pheromone models. These models were developed to forecast congestion on roads with signalized intersections by considering only green and red phases. Motivated by this issue, three short-term traffic forecasting models are proposed: (i) Extended Pheromone Model (EPM), (ii) Extended Pheromone Model with epsilon-Support Vector Regression (ε SVR-EPM), and (iii) Extended Pheromone Model with Artificial Neural Network and Particle Swarm Optimization (ANNPSO-EPM). It is worth noticing that EPM is an algorithmic model whereas the other two are machine learning models. In all proposed models, a new color pheromone concept is proposed with two significant contributions. First, the color pheromone concept is developed to capture stochastic traffic conditions on the roads with non-signalized intersections. Second, the proposed concept is further extended to include all three color phases (red, yellow and green) to forecast dynamic changing traffic behaviors for roads with signalized intersections. The proposed color pheromone concept in EPM, ε SVR-EPM, and ANNPSO-EPM is different from the existing models as it dynamically switches its computation techniques based on traffic light phases. All three proposed models can be realized through a Pheromone-based Multi-Agent System composed of Vehicle Agents and Intersection Agents coordinating locally with one another To promote practicality, Singapore City Hall map is employed in a microscopic simulator of Simulation of Urban Mobility (SUMO), showing that all proposed models outperform the other existing pheromone models. [ABSTRACT FROM AUTHOR]

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi-agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies. • A two-stage stochastic model considering uncertainties is proposed. • A multi-agent simulation system is created to mimic the daily electric bus operations. • A reinforcement learning method and a surrogate-based optimization are proposed. • Experiments are performed to seek managerial insights for the operators. [ABSTRACT FROM AUTHOR]

The modelling of mode and route choices of public transport passengers is an essential component of travel demand modelling and transportation planning. Traditionally, choice models are trained using data from revealed and stated preference surveys which are not only cost and time intensive but also suffer from bias due to limitations in sample size. In this article, we present a novel approach utilizing a combination of emerging data sources for non-private modes, namely public transit smart card data, taxi GPS trajectory data, and taxi trips transaction data in order to calibrate an integrated taxi and transit mode and route choice model. We apply our approach for a case study of the taxi and public transport system of Singapore using data sets obtained from the local transport authorities. We solve the first mile and last mile data gaps by overlaying the individual transport nodes against geospatial land use data in order to identify their actual origin and destination points of each journey. To model the behavioural inter-dependencies between the choice of taxi and transit options, we tested a two-level nested logit model and a cross-nested logit model. Along with the commonly included exploratory variables such as in-vehicle travel time, transfer time and number of transfers, our model also incorporates differences in travel cost and separate mode specific constants for peak and off-peak periods. Generic and mode-specific in-vehicle travel time and cost coefficients are tested in the utility functions for the transit and taxi alternatives. Willingness to pay estimates are calculated and compared against similar estimates from Singapore. We also present an application of the model by predicting the mode split between taxi and transit under selected transit and taxi fare change scenarios. Our modelling methodology is highly generalizable and can be applied to other cities with similar data availability. [ABSTRACT FROM AUTHOR]

• Dockless bike-sharing mobility presents a high degree of spatiotemporal regularity. • Use Poisson regression to analyze the trip generation/attraction factors. • A majority of trips are started from or ended at metro stations. • Flow imbalance is observed with more trips towards metro stations. • Cycling "communities" are locally clustered and consistent across the four months. Many cities around the world have adopted dockless bike-sharing programs with the hope that this new service could enhance last-mile public transit connections. However, our understanding of the travel patterns using dockless bike sharing is still limited. To advance the knowledge on the new service, this study investigates mobility patterns of dockless bike sharing in Singapore using a four-month dataset. An exploratory spatiotemporal analysis is conducted to show daily travel patterns, while community detection of networks is used to explore the spatial clusters emerged from cycling behaviors. A series of Poisson regression models are then estimated to characterize the generation, attraction and resistance factors of bike trips in different periods of a day. The proposed regression model, which considers built environment factors of origin and destination simultaneously, is proved to be effective in deciphering mobility. The empirical findings shed light on policy implications in sustainable transportation planning. [ABSTRACT FROM AUTHOR]

• Model how subjective evaluation of existing modes influence autonomous mobility-on-demand (AMOD) adoption. • Model impact of inertia from existing travel modes on AMOD choice. • Find that subjective evaluations and inertia both predict mode choice. • Particularly, positive evaluations and current use of ridehailing are strongly predictive of AMOD choice. As autonomous vehicle (AV) technology advances, it is important to understand its potential demand and user characteristics. Literature from stated preference surveys find that attitudes and current travel behavior are as or more important than demographics in determining intention to purchase or use AVs. Yet to date no study has looked at how attitudes and use of existing modes both simultaneously affect AV adoption. In this study, we conduct a stated preference survey in Singapore to investigate how the subjective evaluation of existing travel modes (attitudes) and inertia based on previous use of existing modes affect the adoption of an autonomous mobility-on-demand service (AMOD). Using a sample size of 2,003 individuals and 11,613 choice observations, we estimate a mixed logit discrete choice model incorporating latent variables capturing subjective evaluations of existing travel modes (determined through confirmatory factor analysis), a two-part formulation of modal inertia, and other trip-specific and socio-demographic variables. Results show that subjective evaluation and use of existing modes both affect the adoption of AMOD. Specifically, people with a positive evaluation of ridehailing and those who are current ridehailing users are more likely to choose AMOD. Additionally, those who are current car drivers are more likely to choose AMOD, while users of public transit were less likely to choose AMOD. Given that ridehailing is the closest existing mode to our hypothetical AMOD service, our results might suggest that how AVs are implemented and their similarity to existing modes may be critical to the formation of attitudes and direction of inertia impacting adoption. Our research provides insights on the potential relationship between AVs and existing modes that could valuable in AV network design and service planning. [ABSTRACT FROM AUTHOR]

• Competition between AV and PT in a first-mile market is evaluated. • Five scenarios with different regulation levels are tested. • Supplies of AV and PT tend to concentrate both spatially and temporally. • AV and PT competition results in higher profits for both operators. • Passengers' generalized costs and system VKT are reduced after competition. Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders—the AV operator, the PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply strategies for AV and PT based on the operators' historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines, Singapore. We find that the competition can result in higher profits and higher system efficiency for both operators compared to the status quo. After the supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station and temporally concentrated to peak hours. On average, the competition reduces the travel time of passengers but increases their travel costs. Nonetheless, the generalized travel cost is reduced when incorporating the value of time. With respect to the system efficiency, the bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometers traveled measured by the passenger car equivalent (PCE), while the AV supply adjustment does the opposite. The results suggest that PT should be allowed to optimize its supply strategies under specific operation goals and constraints, and AV operations should be regulated to reduce their system impacts, including potentially limiting the number of licenses, operation time, and service areas, which makes AV operate in a manner more complementary to the PT system. Providing subsidies to PT results in higher PT supply, profit, and market share, lower AV supply, profit, and market share, and increased passenger's generalized cost and total system PCE. [ABSTRACT FROM AUTHOR]

Right-turn waiting area (RWA) is a short demarcated queueing area ahead of the stop line that allows the right-turn vehicles at signalised junctions under the permissive filtering signal operation to proceed into the junction-box at the onset of full green signal phase. The RWA layout gives guidance to vehicle placement of turning vehicles which improves safety and mitigates vehicle queue overflow of the right-turn vehicles. RWA enhances the capacity of right-turn lanes while alleviating conflict severity in some cases. This study analysed the safety impact of the conflict between opposing straight-through vehicles and right-turn vehicles at RWA junctions in Singapore. A microscopic simulation model based on Fuzzy Cellular Automata (FCA) was developed. Field surveys were carried out to obtain the inputs for calibrating the fuzzy inference system. Different scenarios were analysed and discussed including different types of junctions with and without RWAs. The proposed model is found to be able to simulate decision-making of individual drivers at RWA or before stop line, such as exiting the queueing area or crossing the stop line when faced with different gaps and velocity of opposing straight-through vehicles., (Copyright © 2018 Elsevier Ltd. All rights reserved.)