Morning Peak Hours Research Articles

Research on the Supply of Shared Bicycles in Subway Hub Stations-Taking the Subway Station of Zhengzhou People 's Hospital as an Example

In recent years, with the rapid development of shared bicycles, it has played a great role in alleviating urban traffic pressure and facilitating residents to go out to a certain extent. However, while solving the problem of the last kilometer of travel, the uneven distribution of shared bicycles in subway hub stations is becoming more and more serious, which has a certain impact on the quality of residents ' travel. This paper takes the supply of shared bicycles in urban subway hub stations as the research object, and observes and summarizes the characteristics of slow traffic flow near the entrances and exits of subway hub stations in the morning and evening peak hours. On the premise of ensuring the right of way for pedestrians, the supply and demand of shared bicycles in the subway hub station and the configuration of shared bicycles in the surrounding space of the subway hub station are comprehensively considered, and the supply of shared bicycles is predicted by the estimation model and the field data of the subway hub station are investigated, taking the subway station of Zhengzhou People 's Hospital as an example to carry out field research and predict the actual demand and supply of shared bicycles in the subway hub. Finally, the problems existing in the research on the supply of shared bicycles in metro hub stations and possible future research directions are summarized.

Analysis of Passenger Car Equivalency (EMP) Values on Road Sections in BandarLampung City (Case Study: Ratu Dibalau Road Section and Damar Island Road Section)

The increase in vehicle volume in Lampung Province has resulted in a decrease in road performance. Capacity evaluation is prevention, determining capacity there are several parameters, one of which is EMP. In this case, the analysis of road section performance refers to MKJI 1997. The characteristics of MKJI 1997 are strongly influenced by the traffic of major cities in Indonesia which are made the place of empirical research in 1991 - 1995. Of course, there are differences in the characteristics that occurred in that year with the current situation. This study was conducted to determine whether there is a difference in the EMP value in MKJI with the current field EMP. This research uses field survey data including traffic volume, road section geometry, and Time Headway data between vehicles. In this study, the emp value is analyzed using the Time Headway method and the data for analyzing the performance of road sections refers to the 1997 Indonesian Road Capacity Manual (MKJI). The results of the emp MC value for the morning and afternoon peak hours on Section 1 are 0.48 and 0.46. Section 2 is 0.46 and 0.44. This value is significantly different from the 1997 MKJI emp of 0.25. for the DS value using emp MKJI on section 1, the results are 0.45 and 0.68. for DS emp calculations on section 0,3 of 0,43 and 1. For section 2 DS emp MKJI of 0.33 and 0.48. For DS emp calculations on section 2 of 0,48 and 0,65. It can be seen that significant differences can be caused by traffic flow conditions, differences in road characteristics, road gemetry conditions, and environmental conditions in 1997 with current conditions. It can also be seen that the greater the emp value will affect the amount of traffic volume so as to make the degree of saturation value greater.

Seasonal analysis of particulate matter and its exposure on urban bikers in Nashik city, India

Introduction: Exposure to Particulate Matter (PM) can cause ill health effects such as coughing, allergies, decreased lung function, chest discomfort and pain. The current study aims to monitor particulate matter concentrations on the highways in Nashik, India and to estimate its exposure to the bikers in the form of Respiratory Deposition Doses (RDDs) with its seasonal variation.
 Materials and methods: Low-cost air quality monitor was mounted on the bike to measure Particulate Matter (PM1, PM2.5 and PM10) concentrations at breathing level. Extensive mobile monitoring was performed on seven highway stretches passing through city limits at morning and evening peak hours for all the weekdays for three seasons.
 Results: The PM concentrations differed on each route seasonally as well as at peak hours in morning and evening. The maximum PM1, PM 2.5 and PM10 concentrations monitored were 119.84 μg/m3, 218.85 μg/m3 and 239.25 μg/m3 respectively on Route R3 in Winter morning. The maximum RDDHD exposure on R5 and R3 in Winter mornings was due to PM10. While maximum RDDTB and RDDAL exposure on R5 in Winter morning and evening was due to PM2.5. Also, the seasonal and particle size effect on RDD has been studied which exhibits higher rise in exposure in Winter mornings due to PM2.5.
 Conclusion: This study reveals that maximum exposure was observed during Winter mornings. The results recommend that seasons have a substantial effect on PM concentrations and their exposure. The minimum exposure was observed in monsoon, followed by summer and the maximum exposure was seen in winters.
 

A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods

Travel time reliability (TTR) serves as a crucial indicator for evaluating the efficiency and service quality of a road traffic network. This paper proposes a multi-task spatio-temporal generative adversarial network (MTST-GAN) model that simultaneously predicts the TTR in morning and evening peak hour periods. The model incorporates multi-graph convolutional networks to extract spatial correlations from travel time data, while long short-term memory neural networks are employed to consider temporal correlations. Additionally, self-attention mechanisms are applied to the proposed MTST-GAN model to further capture spatial and temporal features. A feature fusion bridge is constructed to integrate the spatial and temporal features learned by each task. Through a numerical experiment conducted on a road network in a Chinese city, our findings demonstrate that the proposed model outperforms several state-of-the-art approaches in terms of Jensen-Shannon divergence, mean, standard deviation, and buffer time indices. Finally, we provide conclusions and suggest areas for further research.

Study on Spatio-Temporal Patterns of Commuting under Adverse Weather Events: Case Study of Typhoon In-Fa

This study focuses on the main urban area of Yangzhou City and conducts a quantitative comparative analysis of traffic accessibility during normal weather and extreme precipitation conditions (typhoon) based on GPS trajectories of buses. From both temporal and spatial dimensions, it comprehensively examines the impact of extreme precipitation on bus travel speed, travel time, and the commuting range of residents in the main urban area of Yangzhou City. (1) Through the mining and analysis of multi-source heterogeneous big data (bus GPS trajectory data, bus network data, rainfall remote sensing data, and road network data), it is found that the rainstorm weather greatly affects the average speed and travel time of buses. In addition, when the intensity of heavy rainfall increases (decreases), the average bus speed and travel time exhibit varying degrees of spatio-temporal change. During the morning and evening rush hour commuting period of rainstorm weather, there are obvious differences in the accessibility change in each typical traffic community in the main urban area of Yangzhou city. In total, 90% of the overall accessibility change value is concentrated around −5 min~5 min, and the change range is concentrated around −25~10%. (2) To extract the four primary traffic districts (Lotus Pond, Slender West Lake, Jinghua City, and Wanda Plaza), we collected Points of Interest (POI) data from Amap and Baidu heat map, and a combination analysis of the employment–residence ratio model and proximity methods was employed. The result show that the rainstorm weather superimposed on the morning peak hour has different degrees of impact on the average speed of the above-mentioned traffic zones, with the most obvious impact on the Lotus Pond and the smallest impact on Wanda Plaza. Under the rainstorm weather, the traffic commute in the main urban area of Yangzhou in the morning and evening peak hour is basically normal. The results of this paper can help to quantify the impact of typhoon-rainstorm weather events on traffic commuting in order to provide a scientific basis for the traffic management department to effectively prevent traffic jams, ensure the reliability of the road network, and allow the traffic management department to more effectively manage urban traffic.

Analysis of Urban Residents’ Travelling Characteristics and Hotspots Based on Taxi Trajectory Data

This study utilizes taxi trajectory data to uncover urban residents’ travel patterns, offering critical insights into the spatial and temporal dynamics of urban mobility. A fusion clustering algorithm is introduced, enhancing the clustering accuracy of trajectory data. This approach integrates the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, modified to incorporate time factors, with kernel density analysis. The fusion algorithm demonstrates a higher noise point detection rate (15.85%) compared with the DBSCAN algorithm alone (7.31%), thus significantly reducing noise impact in kernel density analysis. Spatial correlation analysis between hotspot areas and paths uncovers distinct travel behaviors: During morning and afternoon peak hours on weekdays, travel times (19–40 min) exceed those on weekends (16–35 min). Morning peak hours see higher taxi utilization in residential and transportation hubs, with schools and commercial and government areas as primary destinations. Conversely, afternoon peaks show a trend towards dining and entertainment zones from the abovementioned places. In the evening rush, residents enjoy a vibrant nightlife, and there are numerous locations for picking up and dropping off people. A chi-square test on weekday travel data yields a p-value of 0.023, indicating a significant correlation between the distribution of travel hotspots and paths.

Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing

Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal. Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study. The recommended PCA combination for the three zones in Beijing was 1000 m_1200 m_1800 m. (2) During the morning peak hours, the density of office and apartment facilities greatly influenced the ridership, with a strong threshold effect and spatial heterogeneity. Our research framework also provides a new way for other cities to determine the scope of Transit-Oriented Development (TOD) and proposes a new decision-making method for improving the vibrancy of metro stations.

Behavioural modelling of metro car choice

Overcrowding in metro systems can exert a negative impact on the travel experience of the passengers and on the system’s capacity. This can affect passengers in different ways, worsening their perception of safety, altering their behaviour, decreasing their satisfaction, and leading to higher travel-time sensitivities. One of the causes of overcrowding can be attributed to some metro cars being selected more often than others, and thus, the distribution of passengers inside the train not being homogeneous. This study adopts a disaggregated approach based on the observed behaviour to understand why some specific metro cars are preferred. The characteristics of passengers and metro cars that generate heterogeneity in the load profiles are identified and the motives behind passenger choices are understood by formulating a hybrid latent-class choice model. The behavioural models consider sociodemographic characteristics, level-of-service information, and attitudinal latent variables. Once a metro car choice model was obtained, an operational micro-simulator of the metro was programmed to evaluate how passengers' choices would be affected by altering variables related to the design of the stations, assessing it based on the current situation. Subsequently, interventions to station layouts are made based on the forecast of the behavioural models, to affect passenger choices and produce more homogeneous passenger distributions in the train. The intervention presented in this study reduced 46.49 h of total travel time during the morning peak hours; the impact was greater when considering individual perceptions. Incentives can be developed for optimising the distribution of passengers in metro cars by understanding passenger choice using a behavioural approach, and therefore, they can improve the passenger experience.

Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment

Abstract This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.

AN ASSESSMENT OF VEHICULAR TRAFFIC CONGESTION ALONG ABUJA-NASARAWA ROAD WITH EMPHASIS ON KARU BRIDGE TO AYA INTERCHANGE

Traffic congestion poses a substantial challenge to efficient road transportation, impacting travel times, fuel consumption, and overall productivity. The Abuja-Nasarawa Road, a critical transportation corridor, faces persistent congestion issues attributed to factors such as increased vehicle numbers, inadequate infrastructure, and poor traffic management. The escalating traffic congestion along Abuja-Nasarawa Road adversely affects the region's productivity and economic development. This study aims to analyze the traffic congestion along the Abuja-Nasarawa Road to improve decision-making regarding road use and transportation system planning. The research design combines qualitative and quantitative methods, utilizing a questionnaire and traffic data collection. The study focuses on the AYA Interchange – Karu Bridge section of the road, a critical stretch facing congestion issues. The methodology involves traffic data collection, including vehicle counts, traffic volumes, and average speeds. Results indicate varying traffic flow rates during morning and evening peak hours. Karu Bridge experiences the highest traffic congestion, with a flow rate of 5433 vehicles per hour during the morning. The qualitative analysis reveals the distribution of road users, with 40% being commercial drivers. The findings reveal a prevalence of traffic congestion attributed to the concentration of economic activities in the city center, leading to increased commuter influx. The study recommends measures such as maintaining and expanding the road network, introducing alternative transportation like a railway service, and completing infrastructure projects like the Apo-Karshi Road.

ANALYSIS OF PEDESTRIAN CHARACTERISTICS CROSSING ALONG ROADS

Pedestrian crossing represents a substantial problem. In Iraq, there are no spaces marked specifically for pedestrians, which causes many conflicts between vehicles and pedestrians that lead to many accidents. The pedestrian death rate has increased recently due to the deficiency in adequate pedestrian infrastructure. However, to date, limited research has measured pedestrian behavior at crossing intersections in Iraq. There is a need to carry out in-depth studies to analyze crossing behavior to increase traffic efficiency and pedestrian crossing safety. Pedestrian crossing behavior is a serious issue to be addressed to provide adequate pedestrian facilities to enhance the pedestrian traffic environment. Road safety can be improved by locating crossing locations at the right locations and enforcing laws for pedestrian crossing. This study analyzes pedestrian crossing behavior in Baghdad City, Iraq, for four intersections at an unmarked crossing in the Central Business District (CBD) area. All required data were collected by video recording and a field questionnaire. Then, the data were extracted from video recordings and classified according to the selected variables. The period for observing the behavior was during the morning peak hours (November; time: 8:00 to 9:00 a.m.) for three days per week during normal conditions. This study examines pedestrian characteristics, vehicle/pedestrian flow characteristics, and traffic environment. Crossing patterns were followed for different gender and age groups. The finding reveals that the mean pedestrian speed is 1.33 m/sec. Also, males have a higher speed than females. The influences of age, gender, group size, and road width significantly affected pedestrian speed. The pedestrian speed decreased as pedestrian age increased. Gender and group size had significant effects on distinct crossing speeds. In addition, there is a weak significant correlation between pedestrian speed and selected variables. The study recommended specific marked places where a pedestrian must be located, and according to the pedestrian speed estimated in this study, a signal control for a pedestrian is recommended to be set up beside the street to organize the crossing with appropriate time for crossing safely.

Research on the Application of Smart City Super Brain in the Upgrading and Reconstruction of Expressway—Take Chengdu Old Chuan-Zang Road as an Example

This paper explores the smart city super brain traffic flow prediction method. Through monitoring and studying the traffic volume and average speed of related intersections in the morning and evening peak hour and one normal hour, a total of 7413 data are collected, of which 5930 are used as training sets and 1483 are used as test sets. Long short-term memory network (LSTM) method is used to predict the changes of traffic flow at important traffic nodes along old Chuan-Zang Road in Chengdu and the changes of vehicle speed at typical time of one day. The predicted results are highly coincident with the actual data, which proves the accuracy and effectiveness of the model. According to the predicted results, through the simulation analysis of the transformation scheme of important nodes, the traffic operation of the study section before and after the transformation is evaluated. The results show that the average delay of vehicles is reduced by 23.82% compared with that before the transformation, and the pollutant emission and fuel consumption are also significantly reduced. LSTM, with its unique advantages in processing sequence data and solving long-term nonlinear problems, provides algorithms and basis for urban road upgrading.

Dynamic Power Consumption in Base Transceiver Station in Urban and Rural Setting in Awka, Nigeria using Multi-Attribute Decision-Making Technique

The goal of this paper was to lower base transceiver station (BTS) dynamic power consumption. A multi-attribute decision-making technique was implemented that reflects the stochastic nature of cellular networks and guarantees cost-effective traffic generation and improved BTS power consumption performance. Every related candidate or attribute that is taken into consideration to help reduce power consumption is given a utility function, and the candidate with the highest utility value is chosen to meet the target. The Utility function that the Technique selects is the weighted sum of the normalized attributes. In contrast to the works of other authors, which lowered consumption by 20%, the developed dynamic power consumption model cut consumption to 25.14 percent. The two BTS of interest were measured, and the results showed that Mgbakwu BTS has two Peak (Busy) Hour periods: in the morning, 9.55Erlangs from 7.30am to 8.30am, and in the evening, 10.7Erlangs from 8.30pm to 9.30pm. The BTS is located in a rural/residential area. The BTS measurement and analysis for the UNIZIK temporary site revealed that it is located in a commercial/business setting with a morning peak (busy) hour period of 19.1 Erlangs between 10 and 11 a.m. 14.6 Erlangs is the afternoon peak hour, which runs from 3.30 to 4.30 p.m. The traffic generated in the BTS under various load situations is reflected in the relevant Power Consumption for both BTS.

Bus Line Shift Behaviour: Evidence of Influential Factors based on Smart Card Data

This paper aims to analyse the factors that influence the behaviour in relation to bus line shift with the support of large-scale smart card datasets, focussing on a case study of the public transport network in Belo Horizonte, Brazil. We developed a binary discrete choice model to assess the factors affecting the changes of the bus line, including the time of day, peak periods, day of the week, number of trips taken by users, type of user, and destination of the trip. We address some research gaps identified in the recent literature by examining factors that influence user behaviour in addition to uncovering urban mobility patterns. The results of a Logit choice model are discussed considering the attributes of the service and of the journey to support evidence-based policy making for adapting bus service design, drawing insights from extensive smart card dataset analysis. Our findings indicate that users are generally inclined to bus line shifts than using the same lines, with such changes occurring more frequently during late hours and inter-peak periods compared to the morning and afternoon peak hours. Additionally, regular users are more likely to change lines than occasional users, and trips with discounts and smart card usage for transfers on trips home tend to involve different lines. Several policy measures can be considered to mitigate passenger discomfort associated with changing bus lines and to reduce operational costs arising from multiple lines serving the same locations. These include investing in promotional efforts to enhance the image of specific bus lines, prioritising certain lines during the day to minimise travel times and retain passengers on the same bus, adjusting line frequencies to reduce the need for transfers, and offering additional incentives to frequent users. Implementing discounts in corridors with shorter travel times can also encourage passengers to remain on the same buses.

Investigating the Nonlinear Effect of Built Environment Factors on Metro Station-Level Ridership under Optimal Pedestrian Catchment Areas via the Machine Learning Method

Exploring the built environment factor’s impact on metro ridership can help develop metro station area planning strategies. This is in order to compensate for the shortcomings of previous studies, which mostly used all uniform pedestrian catchment areas (PCA) around metro stations. Beijing was divided into two zones and 12 built environment explanatory variables were selected as independent variables based on the “7D” dimension of the built environment. The boarding ridership during the morning peak hours was used as the dependent variable. Nineteen PCA radii from 200 to 2000 m were assumed. The optimal PCA of metro stations for each zone was determined by using the eXtreme Gradient Boosting (XGBoost) model with the objective of minimizing the Mean Absolute Percentage Error (MAPE). The nonlinear impact of the built environment factor of each zone on metro ridership is analyzed under the optimal PCA of metro stations. The study results show that (1) the optimal PCAs of metro stations inside the 4th Ring Road and outside the 4th Ring Road are the circular buffer zones with a radius of 800 m and 1300 m, respectively. (2) There is a nonlinear influence of the built environment factor on metro ridership, with strong threshold effects and spatial heterogeneity. The PCA results can be used for the built environment’s zoning of metro stations. The XGBoost model and the nonlinear impact results provide significant implications for the practice of station-level ridership forecasting and integrating TOD development and built environment renewal.

Exploring the impact of trip patterns on spatially aggregated crashes using floating vehicle trajectory data and graph Convolutional Networks

In recent years, increased attention has been given to understanding the spatial pattern of crashes in urban areas. Accurately capturing the spatial relationship between crash counts and variables requires extracting hidden information from multiple data sources. In this study, we propose a machine learning model to explore the spatial impact of activity patterns on spatially aggregated crash counts. Our paper introduces a two-step framework: (a) the Latent Dirichlet Allocation (LDA) model, an unsupervised method for mining hidden activity patterns from floating vehicle trajectory data, and (b) the Graph Convolutional Network (GCN) model, which builds the spatial relationship between multi-source data. The data and hidden activity patterns were aggregated into 175 Traffic Analysis Zones (TAZs) in San Francisco using spatial partitioning. The GCN model provided higher prediction accuracy than commonly used machine learning algorithms that did not consider combined spatial relationships and those that only considered traditional vehicle counts data. Furthermore, we used attribution algorithms to obtain the respective weight scores of each factor. Our results reveal that daily vehicle kilometers traveled, road density, population density, commercial activity during weekends, and residential activity during morning peak hours on weekdays are factors associated with crashes.

Analisis Pengaruh Aktivitas Pasar Tradisional Marisa Terhadap Kinerja Ruas Jalan Sultan Amai Kecamatan Marisa Kabupaten Pohuwato

The aim of this research is to determine the influence of Marisa Traditional Market activities on the performance of Marisa District Roads, Pohuwato Regency. The research was carried out at the Marisa Traditional Market, Jalan Sultan Amai, Marisa District, Pohuwato Regency using data analysis, namely vehicle volume (Q), free flow speed (Fv), capacity (C), degree of saturation (DS) and service level. The research results show that the vehicle volume at the morning peak hour is 373.53 vehicles/hour. The vehicle volume during the afternoon peak is 363.27 vehicles/hour and in the afternoon it is 450.10 vehicles/hour. Traffic speed (Fv) is 37.04 km/hour. The capacity value (C) is 2,249.84 pcu/hour. The degree of saturation (DS) value was 0.16 for the first day of observation, 0.17 for the second day of observation and 0.20 for the third day of observation. From the degree of saturation value, the level of service (LoS) value is obtained, namely A. Based on the characteristics of the level of service, this means free flow, low volume and high speed. The driver can choose the desired speed.

Spatial and temporal variability of urban cyclists’ exposure to PM2.5 in Medellín, Colombia

Fine Particulate Matter (PM2.5) has become a major issue in cities around the world as it adversely affects human health and the environment. This study aims to develop a deeper understanding of the impact of bicycle lanes designs on cyclist exposure to air pollution in a developing-country city. PM2.5 concentrations were measured along a predefined route with different bicycle lane designs in the city of Medellín, Colombia. The measurement campaign was made between October and December 2020 during peak and off-peak hours on weekdays, where a total of 29 bicycle trips were carried out. To obtain accurate measurements, we used a laser-based particle monitoring system. The study's findings reveal that the bicycle route section without dedicated bicycle lanes had the highest PM2.5 exposure and inhaled dose per kilometer traveled. Furthermore, the mean PM2.5 exposure for cyclists during the morning peak hours was higher (33.8 μg/m3) compared to the evening peak (16.1 μg/m3) and off-peak hours (11.1 μg/m3). The inhaled PM2.5 dose was three times higher during morning peak hours than during off-peak hours and twice as high during evening peak hours. These results show that segregated cycling lanes on the sidewalk can considerably lower PM2.5 exposure and inhaled doses for cyclists when compared to other lane designs, highlighting the significance of infrastructure development in supporting sustainable transportation and public health.

Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area

ABSTRACT Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.

Measuring the impact of COVID-19 policies on local commute traffic: Evidence from mobile data in Northern California

The COVID-19 pandemic upended the world economy and significantly altered how people work by imposing limits on in-person interactions. The policy response in places like California illustrate a range of scenarios that are applicable more broadly, from stringent restrictions on travel to flexible directives. This study focuses on the impact of various policy instruments the State of California implemented on morning peak hour and home-based work traffic volume in the San Francisco Bay Area and California’s Central Valley. Previous studies have not been able to measure COVID-19′s impact on commute traffic at a sufficiently local level, nor have they tested the impact of policy instruments on local commute traffic. We fill this gap by using mobile-derived morning peak hour traffic volume data from StreetLight Insights along with census-based neighborhood socioeconomic characteristics to model neighborhood-level variation in morning traffic volume and distance traveled. Remote work, a policy the private sector initiated, had a more significant and lasting effect than state policy. Morning traffic volume did not recover in response to policies aiming to provide businesses and people greater flexibility and predictability, and while vaccination was associated with a recovery of morning peak hour and home-based traffic, that recovery was short of pre-COVID levels. The effect of remote work undergirds inequities in commute burden. Economic sectors deemed essential and lower-income workers saw the lowest decreases in commute traffic and fastest recovery. Policymakers and planners ought to consider the occupation mix as a key ingredient in understanding the impact of telework ability and takeup on future transportation infrastructure improvements across various regions.