Public Transport Stop Research Articles

Identifying patterns of change in traffic flows’ parameters depending on the organization of public transport movement

This study examines the issue of traffic organization on sections of city streets, in which there are public transport stop zones of different configurations (with a drive-in pocket and along a traffic lane). Accordingly, the object of research is the parameters of traffic flows; the subject is the regularities in their change on such specific sections of the street-road network, taking into account the configuration of stops, the number of traffic lanes, and priority conditions for the movement of public transport. The task addressed in this work was the quantitative assessment of the efficiency of traffic management in public transport stop zones. It was also important to compare different configurations of street segments in terms of their impact on delays of private and public transport. As a result of the research, the speed and time characteristics of the traffic flow and their dependence on its composition and intensity for different number and specialization of traffic lanes, as well as the location of the public transport stop, were determined. The resulting quantitative indicators of delays of all types of transport for each modeling option made it possible to determine the best of them according to the criterion of minimal time loss. This applies to both private and public transport, as the values of delays may differ from each other by 10–50 %. An applied aspect related to using the scientific result is the possibility of justifying the number of traffic lanes, or their allocation for public transport, as well as determining the appropriate configuration of stops. This constitutes the prerequisites for the transfer of the scientific results to the relevant interested structures in the field of transport, which are responsible for the organization and safety of traffic under urban conditions

Research concerning air quality in the vicinity of a public transport stop

Abstract The research focuses on particulate matter and the results of their concentration in open air due to several impact factors like traffic intensity and weather conditions. The result of the 11 days episode measurement is provided in graphics. The study is part of a complex research involving the development of an integrated system for analysing air quality in the vicinity of public transport stops. Also, the number of passengers exposed to levels of poor air quality which are waiting in these stops will be recorded. The authors analysed a road section where future integration and applicability of such a system is presumed to become functional, recording the impact on both passengers and environment.

Active travel, public transport and the built environment in youth: Interactions with perceived safety, distance to school, age and gender

IntroductionThis study examined interactive effects of the built environment, perceived safety, age and gender on active travel and public transport use among adolescents for school journeys. MethodThis study used cross-sectional data from 440 adolescents (mean age 15.4 ± 1.5 years, 58% girls) in Melbourne, Australia. Adolescents self-reported their school travel modes, perceptions of traffic and personal safety. Exclusive active travel and public transport use to/from school were determined. Objective built environment features around home and school (1 km buffer) were generated. Multilevel logistic regression was used to estimate main and interactive associations. ResultsOverall, 18% travelled exclusively by active travel and 32% by public transport. Distance to school was negatively associated with exclusive active school travel (OR = 0.44, 95%CI: 0.34, 0.56). Distance to school moderated the association between some built environment attributes (land use mix, residential density around home, distance to public transport stops) and odds of active school travel. There was a positive association between residential density around school and active school travel among those who perceived their traffic environment to be safer. Boys were more likely than girls to use public transport if they lived further from school. A negative association between distance to a public transport stop from home and the odds of public transport use was observed among those who perceived their environment to be less safe. ConclusionThe findings highlight the complexity of influences on mode choice for the school journey. Distance to school remains one of the most important predictors of active travel/public transport use. Supportive built environment attributes, such as a diverse mix of land use around home had facilitating effects on active travel among adolescents who lived closer to school. Multilevel interventions that target both the environment and perceived safety are needed to promote active travel and public transport use among adolescents.

Built Environment and Gender-Based Vulnerability as Key Drivers of Food Insecurity in Allegheny County.

Food insecurity is pervasive in Allegheny County, as one in five residents experiences food insecurity. Food insecurity is linked to chronic health conditions like heart disease and hypertension and disproportionately affects women in the United States, particularly women who are head of household. There are multiple dimensions used to measure regional disparities in food accessibility. Prior research has examined the linkages between food access and food insecurity, and this study aims to explore further the relationship between equitable access to sustainable and affordable food sources. This study examines food outlets in Allegheny County to determine if there is a significant relationship between food outlet availability and food insecurity. Both the presence and accessibility of these food outlets were examined. To measure accessibility, the walking distance to the nearest public transportation stop was calculated for each public transportation stop. The minimum distance to each food outlet was compared to food insecurity rates on a census tract level. Results showed that communities without grocery stores had lower access to healthy and affordable food sources. Also, communities with a higher proportion of female-headed households experienced greater food insecurity, regardless of access to food outlets. There was no statistically significant relationship between the distance from public transportation stops to grocery stores and rates of food insecurity overall and in low-income communities. However, communities with inaccessible grocery stores, either absent in the census tract or without close public transport stops, did have even greater average rates of food insecurity if there was an above-average proportion of female-headed households. Based on these findings, it is evident there exist structural elements of the built environment that correspond with disproportionate rates of food insecurity experienced by communities with households that are predominately female headed. In addition to resource support for these marginalized groups, we suggest that sole reliance on distance as an indicator of food insecurity can be misleading. There should be a greater focus on walkability aggregated on a household or individual level within the community instead of physical distance alone at a general scale.

Crowdedness information and travel decisions of pedestrians and public transport users in the COVID-19 era: A stated preference analysis

The COVID-19 pandemic has impacted people's everyday lives, as avoiding being in crowded places became the number one societal rule. Crowdedness has therefore increasingly affected decisions such as a place visit via a specific path, the selection of a public transport stop, itinerary, etc., thereby making related information increasingly relevant. The objective of this study is to examine the route and travel choices of pedestrians and public transport users, with the provisioning of travel information related to crowdedness levels. To that end, a choice experiment was designed to elicit travelers' preferences. Discrete choice models were estimated based on data collected from 465 individuals in Greece. Results showed that crowd avoidance plays a significant role in shaping mobility decisions for both pedestrians and public transport users. Factors such as place of residence, age, the importance of COVID-19 measures and arrival time are found to affect the likelihood of switch routes in response to information about high levels of crowdedness.

GPS-Based Trip Phase and Waiting Time Detection to and from Public Transport Stops Via Machine Learning Models

Recognizing passengers’ trip phases can provide valuable insights for urban planners in making decisions regarding urban planning, as it involves identifying the various stages of a passenger's journey. There is a significant research gap in current trip phase recognition research. Most existing works rely on traditional techniques such as GIS, surveys, and direct observation at stations to handle the task. The goal of this paper is to present a novel approach to determine the time and distance of access and egress trip phases and waiting time at bus stations, using a machine learning algorithm based on raw GPS trajectories. Specifically, we train a random forest model using two large datasets, Geolife GPS trajectory, and Sussex-Huawei Locomotion dataset, to detect the various transportation modes. Furthermore, a new algorithm is developed for detecting access time/distance, egress time/distance, and waiting time. Our approach is the first investigation in trip phase recognition that combines two large datasets and a machine learning model for trip phase detection. Our study yields the following accuracies on test trips saved in Roma: access time and distance predicted with 80.68% and 91.61%, while egress time and distance arrived at 72.63% and 70.68% accuracy. Passenger waiting time predicted from raw GPS data as a new feature with 82.01% accuracy at the bus station. These results underscore the effectiveness of our approach in predicting different phases.

Estimation of informal park-and-ride

Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam.

Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna

Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7%–14%) are replaced by SAEVs, generating CO2 emissions reductions of 5%–11%. Moreover, 23%–35% of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10%–20% of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2–4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17%–20% of car trips being replaced by SAEVs and generating CO2 emissions reductions of up to 32%.

Design effects of cycle infrastructure changes: An exploratory analysis of cycle levels

IntroductionCycling infrastructure policies yield health and environmental benefits. However, design variations may impact intervention effectiveness. This study explored relationships between infrastructure form (e.g., width, length) and function (e.g., access, safety) and cycling behavior. MethodsWe analysed data from a study evaluating 15 streets with new cycling infrastructure improvements in Paris and Lyon. Comparison streets were chosen based on having similar pre-intervention cycling trends and periods of data availability as intervention streets. The outcome was the difference in daily cycle counts between intervention and control streets. We conducted a virtual street audit with Google Street View to assess 14 street features and derived a function score based on 7 components. We used induced smoothed LASSO regression to identify relevant form or function variables associated with cycle count changes. ResultsThe effects of new cycling infrastructure varied, with significant increases at half (7/15) of the sites and no change at the rest. For every 1 SD increase in cycle lane length, an increase of 83 cycle counts per day (95% CI 32, 134) was observed. Removing car parking and traffic lanes were associated with an increase of 197 counts (108, 285) and 154 counts (58, 249), respectively. Adding a public transport stop showed a negative association with a change of −83 counts (−158, −8). Functions positively associated with cycle count changes were improving safety (75, 95% CI 8, 141) and space (72, 95% CI 10, 135). ConclusionsThis study sheds light on cycling infrastructure design influences on cycling behavior. These insights can help guide future policies and infrastructure development to maximize cycling benefits for health and the environment.

DOSTĘPNOŚĆ PRZESTRZENI NAUKI – NAUKA O DOSTĘPNOŚCI

The purpose of the presented research is to identify problems with the orientation and accessibility of the fragment of the "Warta" Campus at Poznan University of Technology and the building of the Faculty of Architecture and the Faculty of Management Engineering (WAWIZ). The new, almost zero-energy building designed by Prof. Sławomir Rosolski meets ecological and pro-environmental requirements to the highest degree and combines them with the perfection of the building's architectural form. The scope of the ergonomic research conducted concerns access to the Warta Campus from the nearest public transportation stop, the route to the WAWIZ building running through the campus, and the first floor of the building. The scope of the research and accompanying workshops also had an awareness-raising (socio-educational) character, ending with a discussion, and were preceded by design classes, where methods were discussed. The results of the accessibility study allowed the identification of the four most significant measures to eliminate existing limitations. The research focused on architectural accessibility only.

Greening urban logistics: Introducing a method to evaluate optimised decoupling hub locations

Sustainable urban logistics has become challenging over the course of the last decades due to logistics sprawl and increased demands of goods in urban areas. To overcome this problem, a fine-grained network of decoupling hubs, separating suppliers and customers in a spacial and timely manner, are needed. The paper at hand introduces a model of how to calculate optimal decoupling hub locations within urban areas with the goal that inhabitants walk or cycle to the next decoupling hub and do not take a motorised vehicle. The study at hand uses the northern district of Zurich as unit of analysis and shows that by using the developed model, 80% of the inhabitants need to walk less than 250 metres to their next decoupling hub, which is within the radius of 100 metres to the next public transport stop. This supports the integration of picking up or dropping off deliveries while commuting.

Simultaneous modelling of access, egress & transit line choice for public transport

A public transport (PT) trip is a multimodal trip by nature: always complemented by an access and egress leg, for example cycling or walking to the PT stop. Introduction of shared modes increases the available access- and egress- options and thereby the PT system as a whole is possibly improved. In order to investigate those effects an extension is made to the traditional PT assignment algorithm, resulting in a simultaneous process of selecting the access-mode, stop choice, line choice, and egress-mode. A key advantage of our approach is that choices for mode chains are made independent on personal characteristics. This enables us to easily add new access- and egress- modes by only adjusting associated travel time and costs. Simulations for a use case in Amsterdam show that this enables us to complement the PT system with shared bikes, which is not possible in current transport models.

Predictors of (Un)Sustainable and Multimodal Commuting in Kaunas

The study analyses the external (distance from home to work and a public transport stop), socio-economic (age, gender, education, income, living with minors, access to a car), and socio-psychological (attitudes, subjective social norms, perceived behavioural control) predictors of the commuting behaviour of persons working in the city of Kaunas. During the study, an online survey was conducted, which was filled out by 228 respondents (of whom 169 (74.1%) women, 59 (25.9%) men; the respondents’ average age 39.52 years). A multinomial logistic regression analysis was performed, the results of which revealed that commuting behaviour was predicted by environmental attitudes towards non-motorised transport and walking, perceived behavioural control towards public transport, instrumental attitudes towards the car and public transport, and access to a car. The study provides useful information about the type of measures promoting sustainable commuting that can be effective in the city of Kaunas.

Application of bayesian networks to estimate the probability of a transfer at a public transport stop

Optimizing transfers during public transport operations is one of the essential components of improving the quality of transport. Several factors influence the passenger's perception of a transfer: from the personal characteristics of the user of transport services to the parameters of the route network, trip characteristics and the design of transfer stops. The method of constructing Bayesian networks was used as one of the effective methods for solving problems of forecasting complex systems to find the relationship between different types of input data that affect the probability of making a transfer at a stop. The need for a transfer arises for a passenger when two reasons are combined: the need to make a trip between two transport areas and the lack of a direct public transport route between these transport areas. The number of needs for trip will depend on the number of residents in the departure zone, and the probability of not having a direct route will depend on the total number of routes departing from this zone. A simulation was carried out in the PTV Visum software environment (on the example of Lviv city) to determine the impact of these factors on the probability of changing at a stop. As a result, data were obtained on the total amount of passenger exchange at the stops of the public transportation system with distribution into the number of passengers disembarking at the stop, the number of passengers transferring at this stop, and the number of passengers going (up to 200 m) to another stop to transfer. The average waiting time for a transfer at a stop depends on both the number of routes passing through the stop and the regularity of traffic. Strict adherence to traffic schedules helps to reduce the average waiting time for a transfer. A comparison of the results of calculating the probability of a transfer at one of the stops using calculations based on field observation data and using modeling was carried out to check the adequacy of the modeling. The calculated probability is 0.16, the simulated probability is 0.12.

Preferences for first and last mile shared mobility between stops and activity locations: A case study of local public transport users in Utrecht, the Netherlands

Shared transport modes can potentially contribute to first and last mile connections of public transport (PT) trips but this remains quite underexplored in the literature. Our study explores the user preferences for shared modes as first and last mile option to connect activity locations. We have focussed on local public transport in the Utrecht province, The Netherlands, which includes bus and tram lines. Its diversity in land use and PT network density, the overall high bicycle usage, as well as the increased proliferation of shared mobility concepts yield promising information which can be a harbinger for future PT integration worldwide. For both the urban and suburban areas of the province, we have designed and conducted a stated choice experiment. Respondents were able to choose from shared bicycles, e-bikes, e-scooters, and e-mopeds to reach their urban destination from a PT stop. For suburban destinations, we also included light-electric vehicles (LEVs), e-cars, and demand-responsive taxi services. Such a complete list of possibilities to travel by shared modes allows comparing the different options and producing trade-offs not available yet in the literature. A sample of 499 respondents (285 urban and 214 suburban PT travellers) considered their first and last mile mode choice of a recent PT trip in light of the new options. Results show that shared (electric-)bicycles and e-scooters are generally preferred over other shared mobility options. The latter specifically targets younger people (<26 years) and travellers towards suburban destinations. Still, a majority of PT users prefers not to use shared modes in the first and last mile. We found that age, current cycling behaviour and weekday/weekend travelling are the most important factors which determine these preferences. We argue that shared bicycles and e-bikes are the most capable modes in providing benefits to PT travellers in this context and, given the relatively low travel time sensitivity, can best be distributed around the most important PT stops.

Nationwide equity assessment of the 20-min neighbourhood in the scottish context: A socio-spatial proximity analysis of residential locations

The 20-min neighbourhood (20 MN) is a method of designing neighbourhoods in such a way that individuals can meet the majority of their daily needs within a 10-min walk (therefore a 20-min return trip) of their home. The Scottish Government have committed to apply the 20 MN concept nationwide, focusing on disadvantaged communities.The aims of this study were to: (1) create 20 MN catchment areas for health, transport, education, social and recreational domains; (2) describe the number of residential locations within 20 MN domain catchment areas; and (3) describe variation in access to 20 MN domains by area-level socioeconomic status and urbanicity.20 MN catchment areas (800-m) were created for 10 domains using road and path network analysis. All Scottish residential locations (n:146,190) were plotted, assigned area-level socioeconomic status and urbanicity. A dichotomised (yes/no) variable was created to identify whether it was within a 10-min walk of individual 20 MN domains.One in five residential locations had access to all 10 20 MN domains (Urban: 28%, Rural: 5%). There was variation in proportion of residential locations that has access to at least one facility by domains; 91% had access to at least one public transport stop and 84% a public open space. There was poorer access to primary care services (42%) and healthy food retailers (50%). Across all domains, access to at least one facility was greater within the most deprived areas.Access to 20 MN domains was greatest in areas where individual health status tends to be worse. A policy focusing solely on improving access to key facilities and amenities for deprived areas may be ineffective in reducing health inequalities. Future studies should assess the quality of facilities and co-location with health damaging facilities, particularly within more deprived areas. Alternative policy approaches may be required for improving access to facilities and amenities for rural communities.

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

Abstract The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.

Social-Ecological Factors Predict College Students’ Physical Activities and Sedentary Behavior

Guided by the socio-ecological model, the aim of this study was to investigate the predictive strengths of individual, social, and physical environmental factors toward different intensities of physical activity (PA; vigorous PA, moderate PA, walking) and sedentary behavior (SB) among college students. A cross-sectional research design was used. There were 287 college students (Mage = 20.75 ± 2.91; 54% female) recruited from a public research university in the Southwestern United States. Participants completed previously validated questionnaires assessing their PA, SB, and perceptions of self-efficacy, social support, and physical environment variables. Correlation and hierarchical regression analyses were performed to examine the associations and the relative contributions of those individual, social, and physical environmental factors to PA and SB, respectively. The findings indicated that self-efficacy, social support from friends, and convenience of using exercise facilities were positively correlated with vigorous PA. Self-efficacy and physical environmental factor such as convenience of using exercise facilities were significantly associated with students’ moderate PA. Physical environmental factors, including convenience of using exercise facilities, satisfaction with neighborhood services, ease of walking to public transportation stop, and detached single-family residence were significantly related to walking, while only detached single-family residence was associated to SB. The results highlight PA interventions may do well to focus on the promotion of individual and environmental variables to increase PA in college students. More evidence is needed to determine the relationships between social ecological factors and SB.

Integration of shared transport at a public transport stop: mode choice intentions of different user segments at a mobility hub

To create an integrated transport system that can compete with and reduce private car usage, we need a better understanding of the transport and user characteristics that relate to people's intentions to use shared and public transport at a mobility hub. For this purpose, this paper describes the results of a survey surrounding the case study of Leyenburg, The Hague in which a scenario of integrating shared mobility at an existing public transport stop is proposed. This study investigates the intention to use shared modes and public transport in a multimodal transport network and the factors and user characteristics that affect this intention. As digital technologies become important in the integration of modalities by offering digital planning and payment options, concerns regarding digital exclusion in transport services are growing. In this paper we developed a digital skills measure to reflects one's ability to perform tasks that are inherent to the digital services seen in the transport sector. Using an ordinal logistics regression analysis, the study has found that the intention to use shared transport is higher for people who are younger, have a high level of education and a high level of digital skills. In addition, having prior experience with shared transport in the past year and currently using multiple means of transportation during the trip are positively affecting the intention to use shared transport. The intention to combine shared transport with the bus or tram during a trip is similar to the intention to use shared transport and is related to similar characteristics, except for education. The intention to use the bus or tram is found to be mainly related to current transport usage and trip-specific factors and not to other user characteristics. For transport providers, the results provide evidence that offering shared motor scooters and bicycles would be an attractive option for young and highly-educated users who intend to combine the use of shared and public transport.

The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier: An evaluation for active commuting behavior

IntroductionPhysical activity is the foundation to staying healthy, but sedentary activities have become not uncommon that ought to be mitigated immediately. The study aims to highlight the role of a transport system that encourages physical activity among users by applying an active door-to-door transport system. Users’ mode choice is studied to understand their preferences for active commuting. The use of machine learning has since been ubiquitous in a myriad of fields, including transportation studies and hence is also investigated towards its efficacy in predicting travel mode choice. MethodologyThe application of the Random Forest (RF) model to identify travel mode choice is explored using the Revealed/Stated Preferences (RP/SP) Survey data in Kuantan City during weekdays. A total of 386 respondents were involved in this survey. The efficacy of the tuned RF models towards predicting the travel mode choice is evaluated via the Classification Accuracy (CA) performance indicator. In addition, a Feature Importance study is also carried out in order to identify significant factors that contribute towards travel mode choice. ResultsThe results from the present investigation demonstrated that the default RF model has acceptable predictability for both training and test dataset of users’ mode choice, with a CA of 70.2% and 69.3%, respectively. Upon identifying the significant features and further refining the hyperparameters of the RF model heuristically, it was shown that with 145 trees, the CA improved to up to 71.6% and 70.1% for both the training and test dataset, respectively. Through the feature selection technique, the most significant features that affect users mode choice are total travel time (TT), waiting time at a public transport stop (WT), region, walking distance from the last stop to destination (WD2), and walking distance from home to the nearest bus stop (WD1). ConclusionsThe study has illustrated the efficacy of the optimised RF in predicting travel mode choice as well as identified the significant factors for the selection. The findings of the present study provide significant insight for policymakers to improve the performance of the public transportation system so that the users will benefit in terms of health and well-being from active commuting.