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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]

There are two problems associated with contraflow operations, namely, the lane configuration problem and the scheduling problem. The former determines the optimal lane reversing scheme for the operation while the latter decides the start time and the duration of contraflow. In this study, both problems are modelled under one bi-level programming model. The upper level problem gives the optimal lane configuration and schedule by minimising the total travel time of the study area, while the lower level is a traffic simulation model that can capture the response of drivers towards the network changes. A hybrid metaheuristic-traffic simulation methodology is suggested to solve the proposed model. An illustrative case study of Singapore arterial network is adopted as an example to test the effectiveness of the proposed model and algorithm. Results show that by considering these problems simultaneously, an overall optimal solution can be obtained compared with solving the problems individually. [ABSTRACT FROM AUTHOR]

Highlights: [•] Road traffic accidents that occurred on three expressway tunnels are studied. [•] Accident rates are found to be highest in the transition zones. [•] Accident rates are much higher in tunnel entry as compared to exit. [•] Spatial differences in accident rates can be attributed to multivehicle crashes. [•] Casualty likelihood is found to be higher in single vehicle crashes. [Copyright &y& Elsevier]

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]

Most automatic incident detection algorithms were successfully developed using loop-detector-based traffic measurements collected from their own localities. But their detection performances were not satisfactory when applied on data collected using a video-based detector system. The video-based detector system is gaining popularity as it was reported to be cost-effective, less prone to damage compared to loop detectors embedded in road pavement, and possesses surveillance capability. It is able to provide the homogeneity of traffic measurements with greater reliability in non-incident situations. In this study, a simple detection rule was used to develop algorithms that use video-based data for detecting lane-blocking incidents. A set of 96 incidents from Singapore's Central Expressway was used for calibrating these algorithms, with another 64 incidents for validation. Two single-station algorithms, named dual-variable (DV) and flow-based DV algorithms were developed. They have similar detection logic, but the latter includes a pre-incident traffic flow condition in its detection framework. On average, the flow-based DV algorithm outperformed the DV algorithm, and both proved to be effective techniques when compared to some existing loop-detector-based algorithms. [ABSTRACT FROM AUTHOR]

The congestion charging schemes in London and Singapore are compared and assessed in the light of guidelines set out in the Smeed Report, published by the UK Ministry of Transport in 1964, and their performance in reducing congestion and raising net revenue. The aim is to draw lessons for other towns and cities considering the introduction of congestion charging. One important result from Electronic Road Pricing in Singapore is that a per‐entry charge is more effective at reducing congestion than a per‐day charge. It is concluded that although Electronic Road Pricing in Singapore is more in line with the desirable properties outlined in the Smeed Report, both schemes are part of a wider package of transport policies, and that is probably the most important reason for their success. The main lesson for other towns and cities around the world considering the possibility of introducing congestion charging is that any such scheme ought to be accompanied by complementary measures that will provide motorists with a valid alternative to the car. [ABSTRACT FROM AUTHOR]

Facing traffic congestion in the Central Business District and enormous demands on scarce land resources by the growing number of motor vehicles, Singapore, a small island city-state the size of Seattle, embarked on a bold decision to reduce road congestion by implementing the famous Area Licensing Scheme in 1975. This was a manual system of tolls for multiple entries into the Restricted Zone. While achieving the intended effect of cutting down on the volume of vehicular traffic in the Restricted Zone, the authors (and others) found that the problem of congestion had merely shifted in time and place. Many changes were implemented, including shoulder pricing (reduced tolls before and after the peak period) to even out traffic flows in 1994, and the Weekend Car Scheme (1991) and Off Peak Car Scheme (1994) to encourage people to use the roads during off-peak hours. The Road Pricing Scheme was introduced in 1995 on a congested highway to familiarize the public with linear passage tolls. In 1998, Singapore discarded the manual system of road pricing in favor of Electronic Road Pricing, which permitted the charging of tolls per entry, based on vehicle size, route taken, and time of the day. This article traces the rationale for the various measures and discusses the successes and shortcomings for the various measures over a twenty-eight year period from 1975 to 2003. [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]

Discusses the traffic volume and travel time of roads in Singapore. Development of a travel time-density model; Description of the free-flow speed and maximum flow values of the model; Estimates of the radial road capacity; Assumption of the traffic-flow theory.

• 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]

Driver performance in responding to the green-amber-red signal change was studied based on a sample of 2316 last crossing and first stopping vehicles collected by unobtrusive observations at 10 junction approaches in Singapore. Two schemes, a speed-distance diagram (S-D) and an acceleration-deceleration (A-D) diagram, were used to demarcate the driving situations; the driver actions, as revealed outcomes of driver decision-making, were mapped onto these diagrams. The speed-distance diagram can give some indication on what a driver would possibly do. The more complicated acceleration-deceleration diagram is useful for diagnosing the appropriateness of the driver actions. An application of the A-D diagram was demonstrated, and several situations prone to red-running were noted. [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]

With the recent resurgence of bicycle traffic in Singapore, conflicts between pedestrians and cyclists appear evident. To cater to both groups' needs, it is imperative to have a practical approach to balance demand and supply. This study introduces a new user-centric acceptability matrix, LOSAM, which traffic engineers may find easy and quick to use. [ABSTRACT FROM AUTHOR]

Navigational collisions are one of the major safety concerns for many seaports. Despite the extent of work recently done on collision risk analysis in port waters, little is known about the influential factors of the risk. This paper develops a technique for modelling collision risks in port waterways in order to examine the associations between the risks and the geometric, traffic, and regulatory control characteristics of waterways. A binomial logistic model, which accounts for the correlations in the risks of a particular fairway at different time periods, is derived from traffic conflicts and calibrated for the Singapore port fairways. Results show that the fairways attached to shoreline, traffic intersection and international fairway attribute higher risks, whereas those attached to confined water and local fairway possess lower risks. Higher risks are also found in the fairways featuring higher degree of bend, lower depth of water, higher numbers of cardinal and isolated danger marks, higher density of moving ships and lower operating speed. The risks are also found to be higher at night. [ABSTRACT FROM PUBLISHER]

This study presents a distributed multi-agent-based traffic signal control for optimising green timing in an urban arterial road network to reduce the total travel time and delay experienced by vehicles. The proposed multi-agent architecture uses traffic data collected by sensors at each intersection, stored historical traffic patterns and data communicated from agents in adjacent intersections to compute green time for a phase. The parameters like weights, threshold values used in computing the green time is fine tuned by online reinforcement learning with an objective to reduce overall delay. PARAMICS software was used as a platform to simulate 29 signalised intersection at Central Business District of Singapore and test the performance of proposed multi-agent traffic signal control for different traffic scenarios. The proposed multi-agent reinforcement learning (RLA) signal control showed significant improvement in mean time delay and speed in comparison to other traffic control system like hierarchical multi-agent system (HMS), cooperative ensemble (CE) and actuated control. [ABSTRACT FROM AUTHOR]

Zero-inflated count models are increasingly employed in many fields in case of “zero-inflation”. In modeling road traffic crashes, it has also shown to be useful in obtaining a better model-fitting when zero crash counts are over-presented. However, the general specification of zero-inflated model can not account for the multilevel data structure in crash data, which may be an important source of over-dispersion. This paper examines zero-inflated Poisson regression with site-specific random effects (REZIP) with comparison to random effect Poisson model and standard zero-inflated poison model. A practical and flexible procedure, using Bayesian inference with Markov Chain Monte Carlo algorithm and cross-validation predictive density techniques, is applied for model calibration and suitability assessment. Using crash data in Singapore (1998–2005), the illustrative results demonstrate that the REZIP model may significantly improve the model-fitting and predictive performance of crash prediction models. This improvement can contribute to traffic safety management and engineering practices such as countermeasure design and safety evaluation of traffic treatments. [ABSTRACT FROM AUTHOR]

In heavy traffic flow conditions, vehicles have limited manouverability which affects the magnitude of response to incident-induced traffic disturbances and how fast changes in these traffic variables can signal the occurrence of an incident. Such characteristics are usually used to formulate a loop-based algorithm. A recent study reported that some existing algorithms were not able to maintain a desired level of effectiveness when these algorithms were used to detect incidents with a video-based detector system. Two new video-based automatic incident detection algorithms, the INdividual Detection Evaluation (INDE) and COmbined Detection Evaluation (CODE) algorithms were, therefore, developed for the detection of lane-blocking incidents in heavy traffic flow conditions using the Central Expressway in Singapore as a case study. The algorithms detect incident-induced traffic speed and occupancy disturbances differently: INDE processes information at each individual detector station and CODE processes information at two adjacent detector stations. Both algorithms outperformed existing algorithms commonly used in incident management systems. Of these algorithms, the INDE algorithm raised fewer false alarms and gave slightly faster incident warnings. However, its performance was relatively less consistent when applied to a validation database. [ABSTRACT FROM AUTHOR]

A fair amount of literature pertaining to pedestrian streets has been produced but for the most part it is on the American and European developments. There has been surprisingly little published research on pedestrian street development in Asia. The purpose of this paper, therefore, is to examine pedestrian streets in Asia, using the city-state of Singapore as a case study. Since the mid-1980s, pedestrian streets have been adopted as a policy in the conservation and planning of Singapore's city centre. The resulting pedestrian streets came in many forms, from a single pedestrian-only street to a whole precinct of several streets. [ABSTRACT FROM AUTHOR]

Focuses on a Web-based information system to stimulate and predict traffic conditions created by professor Der-Horng Lee and his colleges at the National University of Singapore in Singapore. Career background of Lee; Limitation of existing simulation software; Modules of the system's architecture.

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]

The article announces the award of grants by Mitsui Sumitomo Insurance Group (MSIG) to four projects from the National University of Singapore (NUS) on senior citizen welfare and traffic safety and four additional grants related to traffic safety in Thailand.

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]

This paper presents the development of models to estimate traffic generated by office and retail (shopping centre) developments in Singapore, based on surveys conducted at 27 representative sites. Of particular interest are the effects of the Singapore road pricing scheme and accessibility to public transport on traffic generated by these developments. Two different approaches were used to estimate vehicle trips generated by developments: weighted average rate and regression analysis. The study found that the mean trip rate for office sites located within the area charged under road pricing was lower than the rate for sites situated outside; however, the difference was not statistically significant. The mean trip rate for retail sites within the charged area during the weekday evening hour was significantly lower than the rate outside. For both office and retail developments there were strong linear relationships between traffic generated and floor area (either gross or leasable floor area). The inclusion of the variable `distance to the nearest Mass Rapid Transit (MRT) station' to represent accessibility to MRT services proved to be significant in explaining traffic generated for office developments but not for retail. Trip rates for office and retail developments were found to be considerably lower in Singapore than the UK and US rates. [ABSTRACT FROM AUTHOR]

Abstract: Motorcyclists are the most crash-prone road-user group in many Asian countries including Singapore; however, factors influencing motorcycle crashes are still not well understood. This study examines the effects of various roadway characteristics, traffic control measures and environmental factors on motorcycle crashes at different location types including expressways and intersections. Using techniques of categorical data analysis, this study has developed a set of log-linear models to investigate multi-vehicle motorcycle crashes in Singapore. Motorcycle crash risks in different circumstances have been calculated after controlling for the exposure estimated by the induced exposure technique. Results show that night-time influence increases crash risks of motorcycles particularly during merging and diverging manoeuvres on expressways, and turning manoeuvres at intersections. Riders appear to exercise more care while riding on wet road surfaces particularly during night. Many hazardous interactions at intersections tend to be related to the failure of drivers to notice a motorcycle as well as to judge correctly the speed/distance of an oncoming motorcycle. Road side conflicts due to stopping/waiting vehicles and interactions with opposing traffic on undivided roads have been found to be as detrimental factors on motorcycle safety along arterial, main and local roads away from intersections. Based on the findings of this study, several targeted countermeasures in the form of legislations, rider training, and safety awareness programmes have been recommended. [Copyright &y& Elsevier]

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]

Red light cameras (RLCs) have been used to reduce right-angle collisions at signalized intersections. However, the effect of RLCs on motorcycle crashes has not been well investigated. The objective of this study is to evaluate the effectiveness of RLCs on motorcycle safety in Singapore. This is done by comparing their exposure, proneness of at-fault right-angle crashes as well as the resulting right-angle collisions at RLC with those at non-RLC sites. Estimating the crash vulnerability from not-at-fault crash involvements, the study shows that with a RLC, the relative crash vulnerability (RCV) or crash-involved exposure of motorcycles at right-angle crashes is reduced. Furthermore, field investigation of motorcycle maneuvers reveal that at non-RLC arms, motorcyclists usually queue beyond the stop line, facilitating an earlier discharge, and hence become more exposed to the conflicting stream. However at arms with a RLC, motorcyclists are more restrained to avoid activating the RLC and hence become less exposed to conflicting traffic during the initial period of the green. The study also shows that in right-angle collisions, the proneness of at-fault crashes of motorcycles is lowest among all vehicle types. Hence motorcycles are more likely to be victims than the responsible parties in right-angle crashes. RLCs have also been found to be very effective in reducing at-fault crash involvements of other vehicle types which may implicate exposed motorcycles in the conflicting stream. Taking all these into account, the presence of RLCs should significantly reduce the vulnerability of motorcycles at signalized intersections. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Singapore has redeveloped its Central Area into business districts and relocated the affected population to new towns. Unfortunately, this strong center policy has hindered the development of employment sub-centers. Most jobs are located in the Central Area, resulting in a spatial mismatch that the government is attempting to address by building a world-class public transport system. However, the poor not only face affordability problems and long travel times for employment, but they are also experiencing a shrinking supply of employment due to economic restructuring. Route tests were conducted, and the results indicate that the poor, who generally choose to travel by bus, have to spend up to 9.8% of their household income per month and 70min per trip from their neighborhoods to the city center. Those who take the hub-and-spoke network have to spend 13.2% of household income and take 60min for similar trips. To increase the job-seeking range of these people, they should be offered concessions to encourage use of the hub-and-spoke network. The government should also build a light-rail transit line to pass through the Central Catchment Nature Reserve to connect employment sub-centers. [Copyright &y& Elsevier]

Road crashes are a leading cause of death and serious injuries both developed and developing countries. Intersections are recognized as being among the most hazardous locations on the roads. Although crashes at intersections form about 35 % of the reported accidents account for about 32% of traffic-related serious injuries and fatalities in Singapore, there is no known study that examines the factors contributing to the severity of these crashes. In this study, the ordinal probit model was applied to crash data from 1992 to 2002 to investigate the role a variety of factors play in determining the severity of intersection crashes. Our study shows that vehicle type, road type, collision type, driver's characteristics and time of day are important determinants of the severity of crashes at intersections in Singapore. [ABSTRACT FROM AUTHOR]

Singapore's Land Transport Authority, formed in 1995, set itself a mission of producing a world class transport system. This paper assesses the extent to which this goal is being achieved. It outlines the structure within which transport decisions are taken and indicates the freedom this gives to develop integrated transport and land-use strategies. It demonstrates the long-term stability in transport strategy from 1972 onwards, and the key contributions to that strategy of land-use controls and travel-demand management measures. The individual elements of the strategy are then reviewed and innovations identified in demand management, public transport service provision and information technology. Finally, the key attributes that have led to the success of the strategy are identified, and it is suggested that many of these are transferable to other cities of the developed world. [ABSTRACT FROM AUTHOR]

In implementing effective remedial treatments at hazardous intersections, it often is necessary to identify the geometric and traffic factors that lead to accident occurrence. However, one particular problem frequently encountered in accident studies is how to distinguish virtually safe intersections with little likelihood of accident occurrence from those that have happened to have no accident due to the random process. To deal with this problem, the "excess" records of zero accident, the zero-inflated negative binomial was used to assign the probability to the accident outcome. Accident data at 104 signalized tee intersections in Singapore over a period of 9 years were employed for model development. The model indicates that uncontrolled left-turn slip road, permissive right-turn phase, existence of a horizontal curve, short sight distances, large number of signal phases, total approach volume, and left-turn volume may increase accident occurrence. On the other hand, right-turn channelization, acceleration section on the left-turn lane, median railings, and more than 5% approach gradient may reduce accident occurrence. Moreover, there is a trend of reducing accidents over the years. [ABSTRACT FROM AUTHOR]

Singapore Mass Rapid Transit (SMRT) operates two train lines with 83 kilometers of track and 48 stations. A total of 77 trains are in operation during peak hours and 41 during off-peak hours. In this article we report on an optimization based approach lo develop a computerized train-operator scheduling system that has been implemented at SMRT. The approach involves a bipartite matching algorithm for the generation of night duties and a tabu search algorithm for the generation of day duties. The system automates the train-operator scheduling process at SMRT and produces favorable schedules in comparison with the manual process. It is also able to handle the multiple objectives inherent in the crew scheduling system. While trying to minimize the system wide crew-related costs, the system is also able to address concern with respect to the number of split duties. [ABSTRACT FROM AUTHOR]

Presents a study which described some of Singapore's research and development activities motivated by the interest in providing wireless broadband access to SingaporeONE. Study of the local multipoint distribution service; Information on the wireless asynchronous transfer mode; Conclusions.

• 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]