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• Subway ridership is modeled at the station level with a mixed-effects growth model. • Several passive data streams are combined to examine intermodal relationships. • Subway use depends on temporal context, station locations and neighborhood features. • Urban mobility services may be used to access stations in the network periphery. • Planning strategies to better integrate modes around subway stations are suggested. Subway systems are an example of how underground space can contribute to developing more sustainable cities. Their use can be encouraged by integrating other urban transportation modes to provide easy access to the stations, thus facilitating intermodal connectivity. However, few studies have addressed data and modeling issues with multiple modes. This gap, mainly due to the lack of appropriate methods, explains why relationships between traditional and emerging modes remain poorly understood; there is still debate about whether they are complementary or competitive. This paper investigates this question by examining interactions between three alternative modes (bikesharing, free-floating carsharing and taxi) and the Montreal (Quebec, Canada) subway over two months. To this end, several operational passive datasets are merged. A two-level growth model, including both fixed and random effects, is then developed based on time series of daily numbers of boardings at subway stations. This longitudinal model quantifies the effect of various variables (calendar, weather, socio-demographic and economic, network connectivity, land use and built environment attributes) on each station use. The correlations between subway boardings and alternative mode destinations are also measured to detect potential intermodal relations. Finally, a further categorical analysis is conducted by period and station type to specify these interactions in time and space. Results reveal that alternative modes tend to complement the subway in the network periphery (as possible access mode to the subway stations), but may compete with the subway in areas closer to the city center. Such findings could help policymakers determine the location of the mobility services to enhance first-mile subway access, as well as the best mode integration around subway stations in planning strategies. [ABSTRACT FROM AUTHOR]

This study aims to address the challenge of developing accurate and efficient parking occupancy forecasting models at the city level for autonomous vehicles. Although deep learning techniques have been successfully employed to develop such models for individual parking lots, it is a resource-intensive process that requires significant amounts of time and data for each parking lot. To overcome this challenge, we propose a novel two-step clustering technique that groups parking lots based on their spatiotemporal patterns. By identifying the relevant spatial and temporal characteristics of each parking lot (parking profile) and grouping them accordingly, our approach allows for the development of accurate occupancy forecasting models for a set of parking lots, thereby reducing computational costs and improving model transferability. Our models were built and evaluated using real-time parking data. The obtained correlation rates of 86% for the spatial dimension, 96% for the temporal one, and 92% for both demonstrate the effectiveness of the proposed strategy in reducing model deployment costs while improving model applicability and transfer learning across parking lots. [ABSTRACT FROM AUTHOR]

Abstract: Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models. [Copyright &y& Elsevier]

The increase in the amount of data collected in the transport domain can greatly benefit mobility studies and create high value-added mobility information for passengers, data analysts, and transport operators. This work concerns the detection of the impact of disturbances on a transport network. It aims, from smart card data analysis, to finely quantify the impacts of known disturbances on the transportation network usage and to reveal unexplained statistical anomalies that may be related to unknown disturbances. The mobility data studied take the form of a multivariate time series evolving in a dynamic environment with additional contextual attributes. The research mainly focuses on contextual anomaly detection using machine learning models. Our main goal is to build a robust anomaly score to highlight statistical anomalies (contextual extremums), considering the variability within the time series induced by the dynamic context. The robust anomaly score is built from normalized forecasting residuals. The normalization of the residuals is carried out using the estimated contextual variance. Indeed, there are complex dynamics on both the mean and the variance in the ridership time series induced by the flexible transportation schedule, the variability in transport demand, and contextual factors such as the station location and the calendar information. Therefore, they should be considered by the anomaly detection approach to obtain a reliable anomaly score. We investigate several prediction models (including an LSTM encoder–decoder of the recurrent neural network deep learning family) and several variance estimators obtained through dedicated models or extracted from prediction models. The proposed approaches are evaluated on synthetic data and real data from the smart card riderships of the Quebec Metro network. It includes a basis of events and disturbances that have impacted the transport network. The experiments show the relevance of variance normalization on prediction residuals to build a robust anomaly score under a dynamic context. [ABSTRACT FROM AUTHOR]

As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models. [ABSTRACT FROM AUTHOR]

Aim: This study investigated and compared the associations between self-reported exposures to individual as well as work-related physical and psychosocial risk factors for musculoskeletal (MS) disorders and the prevalence of MS symptoms in different body areas among short- (P&D) and long-distance (Bulk delivery) truck drivers working for the same large gas delivery company in Canada.Methods: 123 truck drivers nationwide participated in this questionnaire-based cross-sectional study. Univariate and multivariate logistic regression analyses were performed.Results: 43.1% of drivers reported MS pain in at least one body area over the past 12 months and 26.8% over the past 7 days. Bulk drivers had a significantly higher prevalence of MS pain than P&D drivers for both periods. When P&D and Bulk drivers were pooled together, belonging to the Bulk subgroup emerged as the strongest factor for low back pain (OR = 8.45, p = 0.002), for shoulder pain (OR = 3.70, p = 0.027) and for MS pain in any body area (OR = 4.05, p = 0.006). In Bulk drivers "High effort-reward imbalance" was strongly associated with MS pain in any body area (OR = 6.47, p = 0.01), with shoulder pain (OR = 4.95, p = 0.016), and with low back pain (OR = 4.51, p = 0.02). In P&D drivers MS pain in any body area was strongly associated with "Working with hands above shoulders" (OR = 6.58, p = 0.009) and "Whole-body vibration" (OR = 5.48, p = 0.018), while shoulder pain was strongly associated with "Hand-arm vibration" (OR = 7.27, p = 0.041).Conclusions: Prevalence of MS pain was higher among industrial gas delivery truck drivers than in the general Quebec male worker population, and higher for Bulk drivers compared to P&D drivers. MS pain in Bulk drivers was mainly associated with psychosocial risk factors and lifestyle; MS pain in P&D drivers was mainly associated with physical risk factors. [ABSTRACT FROM AUTHOR]

Travel to intercity transportation hubs, such as railway stations and airports, can be the most troublesome and inefficient part of the entire air/railway travel journey, as travelers often carry large luggage and have stringent arrival time requirements. Taking public transportation, such as metro and bus services, is inconvenient to carry luggage and less reliable in arrival time while taking taxi services could be less economical. As a result, providing reliable and convenient yet economical on-demand first-mile services for travelers to intercity transportation hubs is essential. This paper proposes a ridesharing approach for the first-mile transport system for travelers heading towards the intercity transportation hub and develops a mixed-integer linear programming (MILP) model with the objective of minimizing the total operating costs for ridesharing service operators. The MILP model considers (1) large luggage that may occupy seats when the car trunk is not large enough to place them; (2) passengers' requirements on arrival time and ride time; and (3) travel time uncertainty ensuring that riders' arrival time and ride time can be satisfied. A tailored adaptive large neighborhood search algorithm with an acceleration strategy is developed for obtaining robust near-optimal solutions within a reasonable time. To assess the solution quality, the MILP model is reformulated as a set-partitioning model, and the column generation algorithm is leveraged to determine a tight lower bound; a greedy algorithm is introduced to obtain an upper bound. Computational experiments on Shanghai South Railway Station demonstrate that ridesharing is an effective strategy for reducing overall travel costs while meeting the first-mile travel demand. In addition, it is essential to consider luggage and travel time uncertainty for determining ridesharing schemes. • The first-mile ridesharing problem to intercity transportation hubs is introduced. • Ride time, arrival time, large luggage, and travel time uncertainty are considered. • Two mathematical models, arc-based and route-based, are proposed. • A robust adaptive large neighborhood search algorithm is designed. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to assess the work-related physical demands of long-distance truck drivers employed by a large gas delivery company in Canada.Methods: A total of 15 truck drivers participated in a data collection that included self-reporting assessments, field observations, and direct measurements to describe daily tasks organization, postural demands, physical workload, and force exertions.Results: Truck drivers' work was characterized by long working days ranging from 9.9 to 15.1 h (mean = 11.4 h), with half (49%) of the total working time spent behind the wheel. The overall workload as measured by relative cardiac strain (18.7% RHR) was found excessive for the long term given the shift duration. Peaks of heart rate in excess of 30 beats per minute above the daily average occurred mainly while operating valves and handling heavy hoses during gas deliveries. The task of delivering gas at a client's site required a moderate work rate on average (8.3 mlO2/kg/min) requiring 24.4% or maximum work capacity on average.Conclusion: Based on multiple data sources, this study highlights the risks of over-exertion and of excessive physical fatigue in the truck drivers' work that are coherent with the high prevalence of self-reported musculoskeletal pain in this group of workers. [ABSTRACT FROM AUTHOR]

Aim: This study assessed the work-related physical demands of short-distance truck drivers employed by a large gas delivery company in Canada.Methods: A total of 19 truck drivers participated in the data collection, which included a combination of self-reports, field observations and direct measurements to report on the work shift task composition, postures, physical workload, and force exertions.Results: Driving (mean of 43% of daily work shift) and delivering gas cylinders to customers (28%) were the main tasks of the truck drivers. Delivering gas cylinders measured as moderate level work and daily work duration was not excessive with respect to mean cardiac strain for most drivers. However, manual handling and force exertion activities were frequent and deemed unsafe most of the time with respect to existing guidelines on manual materials handling.Conclusion: This study documents physical risk factors that are consistent with musculoskeletal pain prevalence reported for short-distance truck drivers. [ABSTRACT FROM AUTHOR]