Public Transport Hubs Research Articles

Transit-Oriented Development in Middle Eastern Cities: The Urban Renewal Framework for the Souq Waqif in Doha, Qatar

Cities around the world are evolving dramatically due to urbanization. Over the last few decades, rapid urbanization coupled with rapid economic growth in Qatar has resulted in transport-related issues such as urban sprawl, traffic congestion, poor walkability, and an increasingly inefficient transit network lacking pedestrian accessibility and connectivity. The related sprawl (due to a lack of compactness), a lack of intricacy, and connectivity have challenged the built environment’s sustainability. The Souq Waqif neighborhood, renowned for its rich cultural heritage and historical significance, serves as the focal point of this study. Its central positioning and status as a prominent social nexus within Doha render it an ideal case study. Specifically, this research investigates the Souq Waqif metro station, evaluating it through the lens of five distinct design aspects: (1) walkability, (2) ground use balance, (3) mixed-use spaces and public spaces, (4) multi-modality and transportation hubs, and (5) public transportation network. To reach this aim, the study methodology uses a qualitative approach informed by the Integrated Modification Methodology (IMM). The IMM framework provides a means to transform underperforming neighborhoods like Souq Waqif, into more cohesive and sustainable communities by retrofitting, renovating, and reactivating the existing urban fabric. Site observations, interviews, and surveys were used to collect data to complement the analysis. Findings highlight the deficiencies in connectivity and multimodal transportation adjacent to the Souq Waqif metro station. In addition, the ability of metro stations to conserve crucial cultural legacy and social identity in rapidly urbanizing cities like Doha is explored. To this end, recommendations are given to increase the efficiency and sustainability of Souq Waqif and the like.

Designing microtransit services in suburban areas: A case study in Palermo, Italy

Poor quality of Public Transport (PT) services is one of the main causes of social exclusion for people living in the suburbs. Public transport companies usually allocate few financial resources to these areas, providing transport services with low frequency, poor accessibility, poor reliability, and high waiting times at stops. Recently, microtransit has emerged as an effective solution to improve the travel experience in suburban areas, particularly for non-commuting trips during off-peak hours. This paper presents an integrated methodological approach for designing microtransit services to meet the mobility needs of people living in low-density suburbs. By conducting a Reveled Preference (RP) and Stated Preference (SP) survey and developing a travel demand model, the demand was estimated and used as input to simulate and size the service. Combining GIS and simulation models, Key Performance Indicators (KPIs) were assessed; fleet size to meet the trip requests was identified and the fare was selected using a sensitivity analysis. The method was applied to a real case study to design a new microtransit service with flexible routes and on-demand stops in a suburban area in Palermo, Italy. The results highlight how introducing a microtransit service with 30 nine-seater vans could change the mobility habits of people living in the suburban area, being attractive and financially sustainable if costing 2 €, or just a little more than the existing fixed-route bus service. It could improve the travel experience by reducing the average waiting time at stops to around 5 min and improve access to amenities and PT hubs by guaranteeing a walking time of maximum about 8 min.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Trip Attraction Rate Estimation Using Open Data Sources for Smart Transportation Planning

Trip Attraction Rate (TAR) serves as a load factor utilized in aggregate transportation modeling. Conventional estimation consumes a lot of time and manpower for field surveys. Meanwhile, open data sources, such as Google Maps, offer an alternative approach to conducting a survey. The popular times feature from Google Maps can estimate coffee shop TAR in the city of Bandung. This study focuses on coffee shops in Bandung City. Data was collected using web scraping techniques on Google Maps and produced a sample size of 377 data points after the validity and reliability processes. Subsequently, multiple linear regression was employed to estimate TAR. The findings reveal that variables like building area and distance to public transportation hubs influence TAR simultaneously. Moreover, our approach could also distinguish TAR between weekdays (0.13 people/m2/hour) and weekends (0.14 people/m2/hour) in Bandung City. This result challenges the usage of Institute of Transportation Engineers (ITE) standards, which is often due to limitations of time and workforce in conducting surveys for TAR estimation. The difference in values indicates the need to estimate specific TAR for each city in Indonesia rather than relying on ITE values, and the proposed approach to using open data sources will shorten the estimation process.

Happiness in urban environments: what we know and don’t know yet

There is no consensus on what makes for a livable urban environment. This requires empirical assessment of the relationship between urban characteristics and the happiness of residents. We took stock of the available research findings, using the World Database of Happiness; 445 findings are considered, from 20 nations over the years 1975–2022. We considered 3 aspects of the urban environment, 1) objectively assessed characteristics, 2) subjective perception of urban characteristics and 3) satisfaction with urban characteristics. Urbanites tend to be happier in places characterized by the following objectively assessed features: a) access to local green/nature, b) access to cultural facilities and leisure amenities, c) access to healthcare, d) access to public goods such as access to sewage and water supply and e) access to public spaces. On the other hand, residents tend to be less happy the closer they live to f) shops, g) public transportation hubs and h) the city center. Subjectively perceived environmental characteristics that go with greater happiness are: i) amenities, j) public goods in vicinity k) playground and sport facilities, while l) perceived air pollution is negatively linked to happiness. Residents were found to be happier the more satisfied they are with m) connectivity and local transport, n) local recreation o) water quality and the, p) environment as-a-whole. Correlations with objectively assessed characteristics. with the urban environment are smaller than with subjective perceptions of the same and differ in direction for living close to q) shops and r) public transportation hubs. While objective closeness to these amenities relates negatively to happiness, subjectively perceived availability and satisfaction with these amenities relates positively to happiness. Most of the available findings are of a cross-sectional nature and do not inform us about cause and effect. This strand of research is still in its infancy. By lack of a sound evidence base, claims about livability of urban settings will remain a matter of subjective hunches and sales-talk.

DEVELOPMENT OF A SOFTWARE SYSTEM FOR SEARCHING FOR LOST ITEMS

The article presents an approach to developing a software system to facilitate the efficient search for lost items. As part of the study, the authors designed an integrated system that includes server, web, and mobile versions, which provides broad opportunities for identifying and returning lost property. In large urban agglomerations, the loss of personal items becomes a common problem. Existing solutions are often limited to preventive measures and do not provide sufficient support after the item’s loss. The lack of an effective feedback and identification system complicates the lost property search process. The developed system uses modern technologies such as Django for the backend, JavaScript for the web interface, and Flutter for creating cross-platform mobile applications. This approach makes it possible to expand the list of system users to the maximum extent. Django, a capable web framework, facilitates reliable data management and integration with various interfaces. The study involved the development of a database to provide storage and processing of all the necessary information. Using REST API ensured efficient interaction between the system’s server, web, and mobile parts. This approach allows different system components to exchange data in a unified format. The system lets users register individuals and legal entities, which makes it possible to use it in a wide range of public spaces. It helps to effectively search for and return lost items on the street and in public places, including restaurants, theatres, and transport hubs where lost items often occur. The software system created in this study addresses the critical problem of losing personal items. Widespread use of this system across various sectors could dramatically improve managing lost and found operations, turning a typically disorganised and ineffective process into an efficient, technology-driven one. The system’s implementation has the potential to significantly reduce the time and resources spent on searching for lost property and increase the productivity of lost property offices. Keywords: software system, item search, location, lost property office.

Investigating the Key Factors Affecting Public Transport Ridership in Developing Countries through Structural Equation Modeling

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

Numerical investigation and dynamics of pollutant dispersion in underground restroom ventilation

Urbanization is expanding in both surface and underground space, where the air quality in underground space restrooms in public transportation hubs is a huge challenge due to variations in passenger’ flow and specific features. The pollution in the underground restrooms mainly comes from the microorganisms and odors of human waste. This paper proposes a back-side air exhaust method for the restroom compartment to enhance pollutants removal efficiency, and an optimal design study is performed to determine the best device position, evacuation pressure, and air exchange rate. In response to research gaps, we introduced the influence of unsteady human breathing behavior and human obstacles coupled with the flow field and the pollutants diffusion in the CFD model. After model verification, we numerically investigated the effect of different ventilation modes. The results show that the top evacuation mode may accelerate the diffusion of pollutants. In contrast, the pollutant concentration around the mouth and the spillage outward from the door cracks are significantly reduced under the new ventilation mode. The standardized and actual concentrations of the pollutant and the normalized exposure index were used to assess the indoor air quality. Finally, we put forward a series of dynamical models to describe the variation of indoor pollutant concentrations over time. The study's results will provide reliable theoretical and numerical support to improve the air quality of public restrooms in urban underground spaces.

Backward and forward simulation of polycyclic aromatic hydrocarbons by the grid-scale model _validation and applications for environmental management

Polycyclic aromatic hydrocarbons (PAHs) associated with fine particulate matter (PM2.5) can cause serious health problems, but data are scarce. We have previously developed a grid-scale model (GSM) to simulate PM2.5-PAH levels on an hourly basis. This study aims to verify the applicability of the model to different years and to reasonably assess the long-term spatiotemporal effects on PAH variation in Kaohsiung. From the resulting database, the annual mean of PM2.5-PAH was found to have decreased from 3.18 in 2005 to 1.78 ng m−3 in 2018, representing an overall decrease of 44%. Thus, this study found that the progressive decrease in PAH levels was closely related to green transport and the gradual tightening of industrial operation regulations. Namely, in the urban area near the central public transport hubs, the estimated PAHs had significantly decreased by 16% (0.37 ng m−3) in 2009 with the operation of the high-speed railway in 2007 and the mass rapid transit in 2008. Near industrial areas, PAHs were reduced by environmental regulations, including air pollution control fees and cap-and-trade policies. By using GSM simulation, this study demonstrated a useful and feasible urban planning tool, and can serve as a reference for future studies related to epidemiology and environmental management.

Advancing Urban Healthcare Equity Analysis: Integrating Public Participation GIS with Fuzzy Best–Worst Decision-Making

This study provides an innovative collaborative spatial decision support system (SDSS) that aims to ensure an equitable spatial distribution of healthcare services. Evaluating the equality of access to health services across different geographical areas is important, as it requires the analysis of various criteria such as the proximity of health centres and hospitals (HCHs), the quality of services offered, connectivity to primary roads, the availability of public transportation hubs, and the density and distribution patterns of HCHs. This purpose is accomplished via the use of geographic information systems (GIS) and multi-criteria decision analysis (MCDA) methods. The proposed model includes the weights of the criteria, which are determined through the ordered weighted average (OWA) and evaluated based on their ORness, which ranges from 0 to 1. Furthermore, this model is improved by the best–worst fuzzy method (F-BWM). This approach produces a spatial map that clearly shows the equity of healthcare systems in urban environments. The findings show that the maximum score observed in this study was 0.38% (with an ORness value of 1), whilst the minimum score recorded was 0.28%. In the most severe scenario (ORness = 0), over 70% of the region shows different degrees of fairness, ranging from moderate to suitable and very suitable conditions. Governments and health authorities can use this information strategically to allocate resources and address inequities in access to healthcare facilities.

Effects of large skylights on lighting and thermal environments in underground public transport hubs: A case study in Shenzhen

Large skylights in underground public transport hubs are designed to introduce natural light and reduce the energy consumed by artificial lighting. Although large skylights can improve the lighting environment of underground public transport hubs, they also increase energy consumption due to solar radiation. Therefore, there is an urgent need to balance the improved lighting and energy consumption associated with large skylights and to determine the optimal design scheme. There are few studies on the lighting and thermal environments of underground public transport hubs, and the optimal design scheme for large skylights is unclear. Therefore, in the current study, field tests were conducted to analyze the lighting and thermal environments of two underground public transport hubs located in Shenzhen: Shenzhen North Station and Futian Station. In addition, multiobjective optimization was carried out for the large skylight at Futian Station. The results showed that most passengers were satisfied with the lighting and thermal environment under large skylights, but they were less satisfied in the summer than in the winter. Therefore, appropriate shading measures should be considered to reduce the effect of large skylights on passengers’ thermal comfort in the summer. Furthermore, the results of multiobjective optimization indicated that the large skylight at Futian Station led to a 28.1 % reduction in annual air conditioning-induced energy consumption, a 31.5 % increase in the lighting performance index (UDI), and a 43.8 % increase in the human thermal comfort index (CTR). The results of this study could provide guidance for the design of large skylights in underground public transport hubs.

Investigating the potential of aggregated mobility indices for inferring public transport ridership changes.

Aggregated mobility indices (AMIs) derived from information and communications technologies have recently emerged as a new data source for transport planners, with particular value during periods of major disturbances or when other sources of mobility data are scarce. Particularly, indices estimated on the aggregate user concentration in public transport (PT) hubs based on GPS of smartphones, or the number of PT navigation queries in smartphone applications have been used as proxies for the temporal changes in PT aggregate demand levels. Despite the popularity of these indices, it remains largely untested whether they can provide a reasonable characterisation of actual PT ridership changes. This study aims to address this research gap by investigating the reliability of using AMIs for inferring PT ridership changes by offering the first rigorous benchmarking between them and ridership data derived from smart card validations and tickets. For the comparison, we use monthly and daily ridership data from 12 cities worldwide and two AMIs shared globally by Google and Apple during periods of major change in 2020-22. We also explore the complementary role of AMIs on traditional ridership data. The comparative analysis revealed that the index based on human mobility (Google) exhibited a notable alignment with the trends reported by ridership data and performed better than the one based on PT queries (Apple). Our results differ from previous studies by showing that AMIs performed considerably better for similar periods. This finding highlights the huge relevance of dealing with methodological differences in datasets before comparing. Moreover, we demonstrated that AMIs can also complement data from smart card records when ticketing is missing or of doubtful quality. The outcomes of this study are particularly relevant for cities of developing countries, which usually have limited data to analyse their PT ridership, and AMIs may offer an attractive alternative.

Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN

Traffic prediction is a pivotal technology in the development of intelligent transportation systems. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model. Initially, shared bicycle travel data is scrutinized for spatiotemporal characteristics. Subsequently, an adjacency matrix is constructed based on the spatial attributes of the data to establish the graph convolutional network. Lastly, global spatial autocorrelation analysis and a coupled coordination model are integrated for validation and augmentation. This model is employed to predict shared bicycle travel patterns near public transportation hubs in the Xiamen Island area. Experimental results demonstrate a high level of precision, underscoring the model's effectiveness in providing valuable guidance for shared bicycle travel. The effectiveness of this method can be further verified in subsequent empirical studies.

Visually activated sprinklers applied in atria to mitigate the interaction of water spray with the smoke layer

This research investigates the fire suppression effectiveness of sprinklers in a large atrium of a public transport hub in Taipei, Taiwan, during a fire using FDS. The thermally-activated sprinkler had the shortcoming of delayed activation, with a thick smoke layer accumulating at the ceiling. Thereafter, the interaction of sprinkler spray with the thick smoke layer would cause a large amount of smoke falling to the floor level, significantly impacting safe egress. The novelty of this study is to couple a sprinkler system with a vision-based fire flame detection system (VFDS) having the advantage of locating the fire instantly to suppress fire. A combination of different atrium heights, fire sizes, and scenarios for a total of 27 cases were investigated. The research results have indicated that VFDS-activated sprinklers can suppress an atrium fire at the early stage, greatly reduce the amount of smoke generation and smoke accumulation. In consequence, the interaction of water spray and smoke layer is largely mitigated to prevent early fall of smoke. When the fire is suppressed, the atrium can maintain a clear height that is crucial to safe egress. The research finding of VFDS-activated sprinklers system is significant for fire safety of buildings with large atria.

Does docked bike-sharing usage complement or overlap public transport? the case of Brussels, Belgium

ABSTRACT This paper complements the existing literature on Bike Share Schemes (BSS) by investigating their use from a spatiotemporal perspective to assess their relationship with public transport (PT). We address these questions through the case of Brussels and its long-standing docked BSS ‘Villo!’. Our study analyses comprehensive (consecutive 12 months) and disaggregated (station level) data on rentals and returns and finds that Villo! is used mostly in dense (although not all) districts also well served by PT. However, temporal structures suggest Villo! overtakes PT at night in vibrant districts and possibly in selected districts with lower PT services over weekends. In addition, Villo! stations at key PT hubs usually do not show specific temporal patterns, which suggests intermodality may work at all times during PT operations. There could be an evening peak effect combined with the Brussels’ topography, but this needs to be confirmed by on-site surveys.

A note on how public transport announcements affect the spread of COVID-19

We investigate the relationship between COVID-19-preventive announcements in public transport hubs and the spread of COVID-19 in Vienna, Austria. Due to its frequent high population densities, the role of public transport is a crucial topic in the field of public health, especially during times of pandemic. Using structural equation modeling, we analyze Google mobility data taken from February to December 2020 and find that changes in the contents of the announcements can influence the pandemic crisis, via their effects on public transport mobility. Interestingly, we find that different announcements affect public transport mobility differently. The impact of these announcements is maintained even after taking the effect of the contemporary government regulations into account. We therefore argue that, in order to fight the spread of the virus, it is important for policymakers to inform the general public about COVID-19-preventive behavior in an appropriate manner. We further suggest that governments take advantage of the large daily flow of people through public transport infrastructure, to disseminate vital information during crises.

First quantification and chemical characterization of atmospheric microplastics observed in Seoul, South Korea

This study is the first report on atmospheric microplastics (MPs) observed in five outdoor environments, including an urban forest, a business center, commercial areas, and a public transportation hub in Seoul, South Korea. Air samples were collected using an active air pump sampler for 24 h in each area only on days without rainfall. All observed microplastics are secondary microplastics, in the form of irregularly-shaped fragments or fibers produced through various degradation processes, rather than being primarily produced like microbeads. The abundance of atmospheric MPs varied depending on the environment (i.e., region, height, and time) from 0.33 to 1.21 MP m-3, with the average number of MPs being 0.72 MP m-3 (standard deviation ± 0.39). MPs in the urban forest was observed to be 27% lower in abundance than that in the urban center which is ∼3 km away. The central business district was observed to have a 25% higher abundance during weekdays than on weekends. Our results show the ubiquity of MPs in various areas from high-rise buildings to forests tens of kilometers away from their direct sources, and a positive correlation between the abundance of MP and human activity. Morphologically, the fragment type (87.4%) predominated over the fiber type (12.6%), and chemically, polypropylene (PP) and polyethylene terephthalate (PET) components accounted for 65% of the total MP. PP polymers were found in all observation sites and contributed to 59% of the total MP fragments. The observed fibrous MPs were mainly composed of PET (72.7%) and PP (18.2%) polymers. Compared to other large cities (Shanghai, Beijing, Paris), Seoul is exposed to low levels of atmospheric MPs and high proportions of PP polymers. This study is limited to atmospheric MPs observed in summer and further investigation of MPs is needed to comprehensively understand the distribution and cycle of MPs based on long-term monitoring of atmospheric MPs.

Determinants of station-based round-trip bikesharing demand

First and last mile connectivity of public transport hubs is a key component in promoting multi-modal travel. The Dutch train station operator (NS Stations) promotes the combination of bike and train by offering a train station-based round-trip bikesharing (SBRT) scheme, known as ‘OV-fiets’, located at train stations throughout the country. This scheme allows users to rent a bike to travel between train stations and their destination and vice versa. The round-trip nature of the SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. Little is known about the determinants of demand for round-trip bikesharing, especially when being integrated into an existing PT scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system using multiple linear regression (MLR) and an in-depth analysis for selected stations. The results are compared with the findings of one-way bikesharing schemes. The results show that for hourly rentals in an SBRT-system, the highest explanatory power is attributed to the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Furthermore, the magnitude of the correlation between the determinants and the hourly demand varies considerably across stations, depending on the underlying demand patterns.

Varied perceptions of environmental impacts from informal public transport hubs in two sub-Saharan African cities (Nairobi and Ibadan)

This paper explored perceptions of the environmental impacts of informal public transport (IPT) in Ibadan, Nigeria and Nairobi, Kenya. Based on a questionnaire survey supplemented by exploratory factor analysis the results revealed unanimous agreement that IPT hubs and the infrastructure around them are perceived as having adverse impacts on local people and their environment. There exist statistically significant differences on the perceptions, including the latent factors underpinning them, at city level and across respondent's backgrounds. The findings imply significant scope for urban planning practice and policy-making to find systematic methods to integrate the knowledge about IPT's perceived environmental impacts, from all urban citizens, as a means to pro-actively enhance their rights and inclusiveness. This will ameliorate a planning culture that often ignores the realities and concerns of the marginalised.

Measuring taxi ridesharing effects and its spatiotemporal pattern in Seoul, Korea

Most studies on dynamic ridesharing, which allows any driver to ferry separate passengers with overlapping routes in a single trip, have focused on developing efficient algorithms. While dynamic ridesharing demands extensive computing infrastructure, this paper suggests a simple taxi ridesharing approach that allows only passengers from certain taxi hotspots to share a ride with another who has a similar destination and measures the effects, spatiotemporal patterns and its benefits. Compared to dynamic ridesharing, the proposed method does not require dedicated driver fleets, networked communication system, or monopoly of all passenger information. We identify taxi pickup hotspots and analyze the spatiotemporal patterns of the simple ridesharing approach. The results show that 48 % of rides from hotspots could be shared, reducing the overall vehicle-km traveled by 1.2 km for each shared ride. We also find that spatiotemporal patterns of the ridesharing could represent urban characteristics. For example, places with high ridesharing potential and low saved-trip distances could imply low public transportation accessibility while areas with high shareability during working hours on both weekdays and weekends could represent public transportation hubs. The proposed method is expected to be useful to identify taxi stands that have high ridesharing opportunities. Policy makers can use our approach to support simple ridesharing scheme. Moreover, with the characterized taxi stands, such as longer saved-trip distance and nightlife peaks, the proposed method could be used as decision support tools for temporary allowed ridesharing. In addition, spatiotemporal patterns of the taxi stands would be used in designing public transportation systems.