An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Meteorological variation in daily travel behaviour: evidence from revealed preference data from the Netherlands

peer reviewedThis study investigates the meteorological variation in revealed preference travel data. The main objective of this study is to investigate the impact of weather conditions on daily activity participation (trip motives) and daily modal choices in the Netherlands. To this end, data from the Dutch National Travel Household Survey of 2008 were matched to hourly weather data provided by the Royal Dutch Meteorological Institute and were complemented with thermal indices to indicate the level of thermal comfort and additional variables to indicate the seasonality of the weather conditions. Two multinomial logit–generalised estimation equations (MNL-GEE) models were constructed, one to assess the impact of weather conditions on trip motives and one to assess the effect of weather conditions on modal choice. The modelling results indicate that, depending on the travel attribute of concern, other factors might play a role. Nonetheless, the thermal component, as well as the aesthetical component and the physical component of weather play a significant role. Moreover, the parameter estimates indicate significant differences in the impact of weather conditions when different time scales are considered (e.g. daily versus hourly based). The fact that snow does not play any role at all was unexpected. This finding can be explained by the relatively low occurrence of this weather type in the study area. It is important to consider the effects of weather in travel demand modelling frameworks because this will help to achieve higher accuracy and more realistic traffic forecasts. These will in turn allow policy makers to make better long-term and short-term decisions to achieve various political goals, such as progress towards a sustainable transportation system. Further research in this respect should emphasise the role of weather conditions and activityscheduling attributes

Avoiding congestion in freight transport planning: a case study in Flanders

A substantial increase in transport intensity for passenger and freight traffic has been observed during the last decades and research confirms that this trend will continue in the years to come. Economic centres have turned into heavily congested areas. The freight transport sector incurs excessive waiting times on the road as well as at intermediate stops (e.g. sea terminals, loading or unloading points). This may cause economic losses and environmental damages. Waiting times may be avoided by taking into account congestion in freight transport planning. Vehicle routing problems arise when several pickup and delivery operations need to be performed, mainly by truck, over relatively short distances [1]. Congestion leads to uncertain travel times on links and uncertain waiting times at pickup or delivery locations. Peak hours may be avoided on congested road segments by changing the order in which customers are served. On the other hand, time slots at customer sites may be renegotiated, creating more flexibility to avoid congestion on the road and at customer stops. The objective of this paper is to estimate the benefits of taking congestion into account in transport planning and to quantify the impact of delivery restrictions on transport costs. A highly congested road network raises the need for robust vehicle routing decisions. Current traffic conditions give rise to uncertain travel times. The reliability of travel time on a route is one of the dominant factors affecting route and departure time choices in passenger transport [2]. Similarly, in freight transport the reliability of travel times may be taken into account when planning vehicle routes. In this paper congestion is modelled as time-dependent travel times. These travel times take into account the dynamics of the time lost due to congestion using the Bureau of Public Roads (BPR) function, which is commonly-used for relating travel times to increases in travel volume [3]. The Time Dependent Vehicle Routing Problem (TDVRP) will be studied as a deterministic planning problem taking into account peak hour traffic congestion. Solution methods for the TDVRP have been focused on heuristic approaches [4, 5, 6, 7]. Kok [8] applies a restricted dynamic programming heuristic to solve a TDVRP. In this paper a heuristic algorithm will be presented to solve problem instances of realistic size. Next, this algorithm will be applied to perform a sensitivity analysis to identify which congestion avoiding strategies have a large influence on the objective function. Shippers may adapt the way they plan their transport as a strategy to avoid congestion. For example, time windows at customer locations may be renegotiated, departure times at the depot may be questioned or the assignment of customers to routes and the order in which customers are served may be changed. The proposed methodology will be demonstrated with a Flemish case study

High prevalence of curable sexually transmitted infections among pregnant women in a rural county hospital in Kilifi, Kenya

Background : Women attending antenatal care (ANC) in resource-limited countries are frequently screened for syphilis and HIV, but rarely for other sexually transmitted infections (STIs). We assessed the prevalence of curable STIs, defined as infection with either Chlamydia trachomatis or Neisseria gonorrhoeae or Trichomonas vaginalis, from July to September 2015. Methods : In a cross-sectional study, women attending ANC at the Kilifi County Hospital, Kenya, had a urine sample tested for C. trachomatis/N. gonorrhoeae by GeneXpert and a vaginal swab for T. vaginalis by culture. Bacterial vaginosis (BV) was defined as a Nugent score of 7-10 of the Gram stain of a vaginal smear in combination with self-reported vaginal discharge. Genital ulcers were observed during collection of vaginal swabs. All women responded to questions on socio-demographics and sexual health and clinical symptoms of STIs. Predictors for curable STIs were assessed in multivariable logistic regression. Results : A total of 42/202 (20.8%, 95% confidence interval (CI):15.4-27.0) women had a curable STI. The prevalence was 14.9% for C. trachomatis (95% Cl:10.2-20.5), 1.0% for N. gonorrhoeae (95% CI: 0.1-3.5), 7.4% for T. vaginalis (95% CI:4.2-12.0), 19.3% for BV (95% CI: 14.1-25.4) and 2.5% for genital ulcers (95% CI: 0.8-5.7). Predictors for infection with curable STIs included women with a genital ulcer (adjusted odds ratio (AOR) = 35.0, 95% CI: 2.7-461.6) compared to women without a genital ulcer, women who used water for cleaning after visiting the toilet compared to those who used toilet paper or other solid means (AOR = 4.1, 95% CI:1.5-11.3), women who reported having sexual debut = 18 years (AOR = 2.7, 95% Cl:1.1-6.6), and BV-positive women (AOR = 2.7, 95% Cl:1.1-6.6) compared to BV-negative women. Conclusion : One in five women attending ANC had a curable STI. These infections were associated with genital ulcers, hygiene practices, early sexual debut and bacterial vaginosis

Impact of urban form on daily travel: a comparative analysis

Along with the emergence of Mega City Regions (MCRs) in Europe, mobility patterns have become increasingly polycentric. Since urban planning issues are especially difficult at this scale, it is important to assess the impact of the different evolutions in Mega City Region on important indicators such as daily travel times and daily travel distances. Therefore, in this study the differences between monocentric and polycentric MCRs in terms of travel distances and travel times for constraint and unconstraint mobility are investigated. To this end, four different MCRs were selected for the study: the Paris and Rhine-Ruhr metropolitan areas and the Randstad and Belgian Mega-City Region. For these MCRs regions, the travel times and distances were derived from the national travel surveys. Special attention was paid to the harmonization exercise based, calculating the daily travel distances for 7 different purposes and 9 different transport modes. With respect to the socio-demographics, the least common denominator was used to define comparable socio-demographics. Results indicate clear differences between the monocentric and polycentric regions, especially with respect to shopping and leisure trips. In addition, some policy interventions could be defined based upon the results of the disaggregation based on the socio-demographics

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables

Knowledge of the Concept Light Rail Transit: Determinants of the Cognitive Mismatch between Actual and Perceived Knowledge

peer reviewedThe Flemish public transport company “De Lijn” is planning the development of a new Light Rail network for medium range distance trips (10 to 40km). A challenge exists in the fact that the concept of Light Rail Transit (LRT) is relatively unknown in Flanders. Therefore this paper explores the knowledge of the concept ‘Light Rail Transit’ among the Flemish population. To investigate the knowledge, two separate binary logit models are estimated to explore the determinants of the overall actual knowledge and the determinants of a cognitive mismatch. The results show that age, sex, public transit use, household size, bicycle ownership and weekly number of shopping activities contribute significantly to the overall actual knowledge of the LRT-concept. Besides, cognitive mismatch is only significantly affected by age and gender. Moreover, the results reveal a serious lack of knowledge of the concept of LRT. Consequently, a successful implementation of the LRT-system in Flanders may be jeopardized and thus it is of crucial importance to raise the level of knowledge. A first option is knowledge acquisition based on experience of the transit network. In this view, it can be a good idea to develop “travel-one-day-for-free” marketing actions. Second, it is important to provide information to the travelers by contriving information campaigns based on the determinants identified by the models. How the campaigns should be constructed from an intrinsic and psychological point of view and deliberating between the methods of communication to reach the various target groups are some important considerations for further research

Surveying activity-travel behavior in Flanders: Assessing the impact of the survey design

peer reviewedEver since car ownership and car use started to increase in Western Europe and the USA, transportation planners attempted to model people’s travel behavior. In the context of the Feathers project a dynamic activity-based travel demand framework is developed for Flanders. In this paper, the complete survey design of the data collection effort required for such dynamic activity-based model is discussed. A mixed survey design of using a PDA application on the one hand, and using traditional paper and pencil diaries on the other hand, turns out to be a very suitable way of collecting detailed information about planned and executed activity-travel behavior of households. The results show that no attrition effects are present, not on the number of out-of-home activities reported, nor on the number of trips reported. Moreover the survey mode (PDA versus paper and pencil) has no direct impact on the quantities investigated. Notwithstanding, it is essential for further analysis on the Feathers data to explicitly take into account mode effects because of two reasons. First, the effect of explanatory variables can be influenced by the survey mode. Second, the variance in the estimation of the quantity investigated can differ significantly. Heteroscedatisc linear regression models provide the required framework to explicitly take into account these mode effects