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Rapid urbanization and modern civilization require sound integration with public transportation systems. In the same time, the volume and complexity of public transportation network are increasing, making it harder to understand the public transportation dynamics. As a first step, understanding the similarity among subway stations is imperative. In this paper, we proposed a semantic framework inspired from natural language processing (NLP) to interpret subway stations as compound words. Specifically, we transplanted context and literal meaning of compound words into mobility and location attributes of stations. Using smart card data, we trained stacked autoencoders (SAE) with designed flow matrices as an embedding method to learn the mobility attributes. Subsequently, to discover the location attributes, we have applied affinity propagation clustering to classify 9 point of interest (POI) categories. Combined with urban planning knowledge, we manage to comprehend the land use meanings of 9 POI clusters. The location semantics is chosen from those categories reflecting its urban land use pattern. By choose meaningful combination of mobility and location semantics for stations’ similarity case studies, we summarized potential applications of this semantic framework.

As a green transport means, electric vehicle (EV) has received widespread attention in recent years and an increasing number of cities have been establishing their EV transport systems including EV fleet management and charging infrastructure. Transport policy-makers are concerning about the system performance of an EV charging system measured by energy consumption and user experience. We in this paper aim to develop an effective method for the expected total energy consumption estimation (ETEC) of EV charging systems deployed in a dense city. To achieve this objective, we firstly introduce the nonlinear charging profile, its impacts on energy consumption as well as user experience, and present an approach for estimating the nonlinear charging time. We then elaborate our method to estimate energy consumption for one-time EV charging by addressing four kinds of energy losses in charging process. Next, charging frequency by considering multi-type EVs, and their heterogeneities and different charging needs for normal and fast charging systems is analyzed in depth. These two together determine the network-wide energy consumption and a pro-rated approach is used to figure out the spatial distribution of energy consumption. A case study of Singapore is conducted in the end to validate the proposed methodology. Numerical results reveal a trade-off between energy saving and user experience, and also demonstrate the importance of considering heterogeneities of driving range, charging preference and daily travel mileage.

The accurate short-term passenger flow prediction is of great significance for real-time public transit management, timely emergency response as well as systematical medium and long-term planning. In this paper, we propose an end-to-end deep learning framework that can simultaneously make multi-step predictions for all stations in a large scale metro system. A sequence to sequence model embedded with the attention mechanism forms the backbone of this framework. The sequence to sequence model consists of an encoder network and a decoder network, making it good at modeling sequential data with varying lengths and the attention mechanism further enhances its ability to capture long-range dependencies. We use the proposed framework to predict the number of passengers alighting at each station in the near future, given the number of passengers boarding at each station in the last few short-term periods. The large quantities of real-world data collected from Singapore’s metro system are used to validate the proposed model. In addition, a set of comparisons made among our model and other classical approaches evidently indicates that the proposed model is more scalable and robust than other baselines in making multi-step and network-wide predictions for short-term passenger flow.

The rapid development of autonomous vehicles (AV) in recent years has drawn the attention of numerous countries in terms of its feasibility for use and deployment as individually-owned vehicles or for large-scale fleet planning and deployment as a mobility-on-demand (MOD) service. Singapore is no exception to this global trend and in her pursuit to be smart and car-lite, numerous efforts are made to have AV trials in place and test out their potential deployment in the city state. As one of the many prerequisites of AV planning, public perception on AV plays a vital role when designing any potential AV deployment scheme. As such, a stated preference survey comprising both online survey and field interviews/surveys, was performed island-wide to understand how commuters in Singapore perceive about different AV-based MOD modes. The logit kernel model is adopted to determine how different preference attributes and key demographic indicators can affect the use of AV-based MOD services over other existing first- and last-mile connection modes. The model results have identified how demographics such as gender, age, housing type, education level and income level can influence the travel mode choice. Also, the impacts brought by individuals’ stated preferences over convenience, privacy and familiarity of ride-hailing apps are also investigated. Such findings can provide useful insight in planning future car-lite towns and implementing AV-based MOD services in these towns.

Recurrent bottlenecks in the urban rail transit (URT) system during rush hours have become a significant concern for both passengers and URT operators. In order to alleviate the resulting p...

We focus on the location choice behavior of new residents in the urban system using the reference-dependent theory approach. Reference-dependent theory offers an alternative analytical fram...

Personal rapid transit (PRT) is a kind of innovative public transport service operated by autonomous vehicles on a dedicated guideway network. In the situation, where PRT guideway network is composed of unidirectional links, the congestion degree of network and vehicle travel distance depend on the arrangement of link directions, given certain traffic demand and network configuration. The motivation of this paper is to propose a method to optimize the link direction of PRT network for enhancing system performance. The proposed optimization problem considers both the vehicle travel time and waiting time at merging nodes as the optimization objective and is formulated as a non-linear programming model. A linear approximation-based solution method is further developed to obtain solutions in an efficient way and the exact solution is achieved via this method. The result shows that the proposed method is effective to find an optimized arrangement of link directions in the network given certain travel demand and network configuration. The computational results of the proposed model in comparison to other models are presented and discussed, which show that the significant reduction of waiting time does not necessarily lead to the high increase of travel time, suggesting the importance of congestion control for merging modes.

Ride-hailing or private hire has been around the globe for a decade now but even less in Asia. Singapore has had more than three revisions to its ride-hailing regulation in the six years since the arrival of the disruptive technology, the most for an Asian city. Often quoted in the list of cities with a commendable public transport policy, Singapore still manages to find a viable and significant position for ride-hailing. Singapore, to a large extent, has formulated a successful model based on current market parameters and, more importantly, an adaptive one that evolves constantly with the continually disruptive technology. But how does this compare to cities around the globe? Global cities have formulated different policy regulations for the sector, with each one of them enjoying varying degrees of success and failure. Utilizing a mixed-methods approach using qualitative and quantitative data, Singapore’s ride-hailing sector was benchmarked with chiefly nine global cities. The qualitative data was analyzed using the 3-element model of the social practice theory as an alternative to conventional behavioral studies, thereby eliminating bias on the commuters and rather shifting focus to the practice. The findings were validated by statistical analysis of quantitative data, namely, trip information from the ride-hailing sector in Singapore and driver earnings. The unique addition of the research to ride-hailing policy is the comprehension of the commonalities and patterns across industrial and technological disruption, practice and policy irrespective of sectoral variations thanks to the utilization of the social practice theory. The first-of-its-kind policy exercise in the sector can be repeated for any city, which is a direct testament to the simplicity and exhaustivity of the methodology, benefitting both operators and investors through equitable policy formulation.

This study investigated the personal rapid transit (PRT) guideway network design problem at a strategic level. PRT is known as an on-demand mobility method serving the first or last mile of travel demand, which can fluctuate significantly during a day. This on-demand property may make the system vulnerable to changes in the pattern of demand. The robustness of the PRT guideway network refers to the capability to fit different demand patterns. The motivation of this study was to take the uncertainty of demand into consideration in the process of network design to enhance the robustness of the PRT guideway network in operation. This problem was formulated as a two-stage stochastic linear programming model. The model was applied to a case study with real travel demand data from Singapore. The results of the comparison study show that the network designed via the proposed model is robust and cost-effective.

With increased urbanization, the rapid growth in the travel demand of rural residents has put enormous pressure on public transit systems. The above-average running length costs long turnover time inevitably. Sometimes the ridership fluctuates with high opportunity cost in the urban segment. The ridership and bus arrival time (BAT) from smart card data (SCD) are introduced to analyze the travel demand and the bus operation conditions. Semi-buspool scheduling is proposed to improve the flexibility and the utility of the intercity bus operation. The pooling is operated at a key station, where the first bus runs along the initial route to the terminal in the central city with the pooling passengers, whereas the following bus turns back as the zone bus route. The optimization object of this scheduling is to maximize the ridership and minimize the transit opportunity cost, in terms of the benefit. The restrictions address both the operation resource and the service level for the passengers, including the waiting time and the load factor. An intercity bus line of 38 kilometers in Beijing is taken as the case study, with 2,718 OD pairs of SCD at the morning peak. Under the novel bus-pooling schedule, the operation efficiency is optimized as the transit ridership can increase by 20.0% with notably opportunity cost decrease.

Ride-hailing or private hire has taken the Singapore transport network by storm in the past few years. Singapore has had more than three revisions of its ride-hailing regulation in the six years since the arrival of the disruptive technology. Often quoted in the list of cities with commendable public transport policy, Singapore still manages to find a viable and significant position for ride-hailing. Cities from around the world are all searching for a model of regulation for ride-hailing that can be elevated as a benchmark. Singapore, to a large extent, has formulated a successful model based on current market parameters and, more importantly, an adaptive one that evolves constantly with the constantly disruptive technology. The experts and regulators of the Singapore transport sector were interviewed in depth, tapping into their opinions and technocratic commentaries on the city-state’s Point-to-Point, or P2P, sector regulation. The data were analyzed using the three-element model of social practice theory as an alternative to conventional behavioral studies, thereby eliminating bias on the commuters and rather shifting focus to the practice. Content analysis utilizing QDA is executed for categorization through fine-level inductive matrix coding to elaborate upon the policy derivatives of the Singapore model. The unique addition of the research to ride-hailing policy is the comprehension of the commonalities and patterns across industrial and technological disruption, practice and policy irrespective of sectoral variations, thanks to the utilization of social practice theory. The first-of-its-kind policy exercise in the sector can be repeated for any city, which is a direct testament to the simplicity and exhaustivity of the methodology, benefiting both operators and investors through equitable policy formulation.

This paper studies the daily storage yard manage problem arising in maritime container terminals, which integrates the space allocation and yard crane deployment decisions together with the consideration of container traffic congestion in the storage yard. The integrated problem is formulated as an integer linear programming model with the objective of minimizing the yard crane operating cost and the yard crane interblock movement cost. A divide-and-conquer solution approach is designed to solve the problem in an efficient manner in which harmony search and constraint satisfaction techniques are employed. Numerical experiments are conducted to validate the performance of the solution approach and the improvement from the integrated optimization method.

This paper investigates the network design problem arising from the regional hazardous waste management system. The problem is to identify the locations of various waste facilities, and determine the transportation routes of hazardous wastes and waste residues between those waste facilities. Aiming at minimizing jointly the total cost and total risk, the problem is formulated as a multi-objective mixed integer linear programming model. By exploiting the advantages of the model, three multi-objective optimization approaches are customized to find highly qualified non-dominated solutions. The effectiveness and efficiency of the approaches are examined both on a hypothetical case and a realistic case.

This paper investigates the transshipment operations between vessels and trains in seaport rail terminals. In order to get a more effective transshipment plan, the schedule plan of trains and the transshipment plan of containers among vessels, yards, and trains are determined simultaneously. To this end, a mixed integer programming (MIP) model is proposed to integrate the two sub-problems with the objective of minimizing the total operation cost, in which the service time window and unloading time requirement of trains are considered. It is demonstrated that some variables in the model can be relaxed without influencing the optimal result. Moreover, a set of valid inequalities are introduced to tighten the constraints. The result experiments show that the enhanced model can be solved more effectively. Then, extensive experiments have been conducted to analyze the influence of several factors, including the handling capacity, the yard capacity, and the value of unit cost. The results report that the handling capacity shows a significant effect on the performance of the transfer plan, and increasing the storage cost of import containers leads to a more effective transshipment plan.

This paper tackles the sea-rail transshipment operation problem of seaport rail terminals, which includes two key sub-problems involved in sea-rail intermodal container transportation, namely train schedule template and transshipment plan of inbound containers. With limited resources and time constraints, operators need to decide the placed-in and taken-out time of trains to coordinate with vessels’ unloading time and the transshipment plan of inbound containers (i.e. the amount of containers that are transshipped between different vessels and trains in each time slot) to maximize the number of direct-transshipment containers while minimizing the storage time and dwell time of inbound containers. The problem is formulated as an integer programming model, and a tailored rolling horizon approach with the adaptive horizon and backtracking strategy is proposed. The performance of the algorithm is verified by a set of instances. The results suggest that the handling capacity of rail-mounted gantry cranes shows a more significant effect on the performance of transshipment operation than other factors. The negative effect caused by insufficient handling capacity could be alleviated by methods like prolonging the feasible service time of trains and enhancing the operational capacity of shunting engines.

This research aims to build a dynamic positioning and tracking method under Ad-Hoc network, to enhance the precision with GPS or compensate the signal while missing GPS situation. The research applies multiple RSSI signals with a novel mathematical algorithm and α-β-γ filter to develop a systematic frame, to locate and track the moving node in reliable range. The results show that, the positioning model for the users has excellent ability for positioning and excellent predictive ability for tracking. This has practical value for potential applications of intelligent transportation systems (ITS), connected vehicles (CV), internet of things (IOT) and automated driving cars.

Daily activity pattern is the reflection and abstraction of actual individual activity participation on daily basis. It carries information on activity type, frequency and sequence. Preference of daily activity patterns varies among population, and thus can be interpreted as personal life styles. This paper advances studies on human daily activity patterns by providing new perspective and methodology in the modeling and learning of daily activity patterns using probabilistic context-free grammars. In this paper, similarities between daily activity pattern—which is defined as activity sequence—and language are explored. We developed context-free grammars to parse and generate daily activity patterns. To replicate people’s heterogeneity in selecting daily activity patterns, we introduced probabilistic context-free grammars and proposed several formulations to estimate the probability of a context-free grammar with daily activity patterns observed in household travel survey. We conducted experiments on the proposed formulations, finding that under proper context-free grammar and problem formulation, the estimated probabilistic context-free grammar is able to reproduce the observed pattern distribution in household travel survey with satisfactory precision. Practically, the proposed methodology sheds light on the issue of generating stochastic and accessibility-dependent choice sets for daily activity pattern models in certain activity-based modeling frameworks.

The path size logit (PSL) aims at capturing correlations between path alternatives by including a correction term, path size, to the multinomial logit (MNL). Several path size formulations have been proposed in the literature and applied to various route choice models extensively in road networks. However, the path size formulation has rarely been studied in the public transport context considering the special characteristics of the public transport network. This paper proposes a new path size formulation for route choice modeling in public transport networks. The new path size formulation accounts for both the correlation attributed to spatial overlapping of path alternatives and the correlation attributed to frequency variations. Direct application of existing path size formation in route choice model estimation and prediction based on smart card data from the Singapore public transport network were used in a comparative analysis of the new path size formulation against an MNL and a base PSL. The results show that the new path size formulation has outperformed other models in both estimation and prediction.

Quay-side berthing congestion is an emerging challenging issue that arises in busy container transshipment terminals and calls for effective management of terminal operations. This paper tackles the berthing congestion problem by introducing a proactive management strategy from the terminal’s perspective that adjusts the calling schedule of feeder vessels in such a way that the quay-side workload distribution in the temporal dimension can be balanced. Such a schedule template design problem is considered simultaneously with another two tactical level decision problems, berth template design (i.e., determining preferred berthing positions for vessels) and yard template design (i.e., allocating storage yard space to transshipment flows). This highly integrated problem is formulated as a set covering model. Heuristic methods based on column generation are developed to obtain near-optimal solutions in an efficient way. Computational experiments on real-world sized test instances demonstrate the efficiency and effectiveness of the proposed approach.

Transit ride quality is important for everyday commuters to keep using public transportation as a preferred mode of travel. Despite well paved and maintained roads, sophisticated buses and modern bus control and monitoring systems, bus passengers, especially standing passengers, are frequently subjected to braking and acceleration actions along the way and sway discomforts along the curves. Modern buses come with rather powerful braking systems that are sensitive to slightest pressure on brakes by drivers. Passenger injuries as well as complaints increase as such. When crowd sourcing via using smartphone apps is employed, anyone ranging from a child to senior citizen can access and register a complaint about a discomfort moment along bus rides. Filtering genuine complaints is a challenge. We study a basic case of a person registering discomfort in a bus ride using the smartphone app developed for this purpose. At the back-end, the app keeps collecting sensor data. Findings provide a basis for developing machine-learning algorithms for big-data analytics of passenger feedback at a large-scale. A proof-of-concept program written based on study findings was used to show how discomfort data can be authenticated and classified.

Transportation Research Part A: Policy and Practice, 69, ISSN:0965-8564, ISSN:1879-2375

Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.

This research proposes and tests a new taxi dispatch policy to improve the existing systems used by taxi companies in Singapore. The proposed method chains trips made by reserva tions at least 30 minutes before the customer pick-up times. In this paper, the taxi dispatching system, Singapore Taxi Advance Reservation (STAR), is defined. A novel trip-chaining strategy based on a customized algorithm of Pickup and Delivery Problem with Time Window (PDPTW) is proposed. The idea is to chain several taxi trips with demand time points that are spread out within a reasonable period of time and with each pick-up point in close proximity to the previous drop-off location. The strategy proposed has the poten tial to lower the taxi reservation fee in Singapore to encourage advance reservations that facilitate trip chaining, which translates to lower taxi fares for customers, higher revenue for taxi drivers, and lower fleet ownership cost for taxi companies.

This paper advances the field of network disruption analysis by introducing an application to a multi-modal transport network, capitalizing on the redundancies and improved connectivity of an integrated metro-bus network. Metro network resilience to disruptions can be enhanced by leveraging on public bus services. To ensure better acceptance among operators and commuters, we focus on introducing localized integration with bus services instead of designing an entirely new bus network to achieve the desired resilience to potential disruptions. This is accomplished by increasing the capacity of bus services that run in parallel with affected metro lines as well as those connecting to different metro lines. Our analysis starts with a network representation to model the integrated metro and bus system. A two-stage stochastic programming model is further developed to assess the intrinsic metro network resilience as well as to optimize the localized integration with bus services. The approach is applied to a case study based on the Singapore public transit system and actual travel demand data. The results show that the metro network resilience to disruptions can be enhanced significantly from localized integration with public bus services.

This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently.

The feeder vessel management problem consists of designing preferred berthing positions and service time for cyclically visiting feeders, and allocating storage yard space to the transshipment flows between mother vessels and feeders. We consider the above three tactical decision problems simultaneously for a container transshipment terminal with an eye toward the quayside congestion and the housekeeping cost of container movements. The integrated problem is formulated as a mixed integer programming model and solved by a memetic heuristic approach. A comprehensive computational experiment is conducted to show the effectiveness of the heuristic and the improvement upon real-world terminal operations.

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: ( a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; ( b) a midterm level that handles agents’ activities and travel patterns; and ( c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

This paper presents an integer programming model for the terminal and yard allocation problem in a large container transshipment hub with multiple terminals. The model integrates two decisions: terminal allocation for vessels and yard allocation for transshipment container movements within a terminal as well as between terminals. The objective function aims to minimize the total inter-terminal and intra-terminal handling costs generated by transshipment flows. To solve the problem, we develop a 2-level heuristic algorithm to obtain high quality solutions in an efficient way. Computational experiments show the effectiveness of the proposed approach.

The quayside operation problem is one of the key components in the management system for a container terminal. In this paper, the integrated models proposed in the previous studies to address the quayside operation problem are examined and one of the potential frameworks is identified. A new method called combinatorial benders’ cuts algorithm is developed to solve the berth-level model in the framework. The computational experiment conducted in this research shows that the proposed approach is more efficient than the branch and cut algorithm embedded in CPLEX.

This paper studies the management of transshipment flows in a container terminal. Management consists of designing a visiting schedule template for feeder vessels and determining storage locations for transshipment containers. A mixed integer programming formulation is developed; it includes the objective of minimizing both the total distance traveled by transshipment flows between quayside and storage yard and the workload imbalance in time. To solve the problem, an algorithm is devised on the basis of Lagrangian relaxation to find near-optimal solutions within short computational times. Numerical experiments are conducted to assess (a) the effectiveness and efficiency of the algorithm and (b) the benefit from adjusting feeder visiting schedule.

This paper discusses the quay crane scheduling problem at indented berth, an extension to the current quay crane scheduling problem in the field of container terminal operation. A mixed integer programming model by considering the unique features of the quay crane scheduling problem at indented berth is formulated. For solution, decomposition heuristic framework is developed and enhanced by Tabu search. To evaluate the performance of the proposed heuristic framework, a comprehensive numerical test is carried out and its results show the good quality of the proposed heuristic framework.

This article explores the quay crane scheduling problem at an indented berth. The indented berth is known as an innovative implementation in the container terminals to tackle the challenge from the emergence of more and more mega-containerships. A mixed integer programming model by considering the non-crossing and safety distance constraints is formulated. A Tabu search heuristic is developed to solve the proposed problem. The computational results from this research indicate that the designed Tabu search is an effective method to handle the quay crane scheduling problem at an indented berth.

The demand for daily commuting that is safe, efficient, and cost-effective has given rise to the increasing use of multimodal public transport system. Single-mode and single-criterion route choice based on travel time or cost has been extensively studied. However, little has so far been reported on an integrated real-time model for multimodal and multicriteria route choice. The multiple criteria model described here is based on travel time, cost, and safety and allows for route choice across different transport modes. This model is implemented by integrating geographic information systems (GIS) with a microscopic traffic simulation system. The multimodal transport network is converted to a single-layer GIS network and the switching delays between different modes of transport are assigned to the related links. Subsequently, the link-based generalized cost from different factors is derived. Real-time traffic conditions obtained by means of simulating traffic conditions are then fed to GIS to find the time-d...

Most routing decisions or diversion plans in the literature are made based on the current traffic situation and little attention is paid to future. Such kinds of greedy strategy consistently suggest en route diversion at any point where the alternative route choice is better than the ongoing one. Hence, in the constantly varying real world, excessive diversions may appear and are much likely to create a sort of ‘zig-zag’ effect, eventually leading to a higher cost. A forward-looking diversion strategy considering both real-time and future information is proposed in this paper, and the potential benefits, limitations and difficulties of several diversion strategies are evaluated through microscopic traffic simulations. The research will help logistics companies to assess the benefits of en route guidance system in their day-to-day operations.

This paper involves a study of signal priority strategies for bus operations using Radio Frequency Identification (RFID) technology enabled devices. An actual suburban network of the Clementi area of Singapore, which including several signalized intersections, was used for microscopic traffic simulations conducted in this study. Based on the experimental results, the proposed system has the potential to improve public bus service level by reducing congestion of bus traffic flow through street intersections.