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

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Pedestrian Prediction by Planning using Deep Neural Networks

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately

An assessment of organic solvent based equilibrium partitioning methods for predicting the bioconcentration behavior of perfluorinated sulfonic acids, carboxylic acids, and sulfonamides

SPARC, KOWWIN, and ALOGPS octanol-water partitioning (log K~ow~) and distribution (log D) constants were calculated for all C~1~ through C~8~ and the straight chain C~9~ through C~15~ perfluoroalkyl sulfonic acids (PFSAs) and carboxylic acids (PFCAs). Application of five established models for estimating bioconcentration factors (BCFs) were applied to the PFSA and PFCA log K~ow~ and log D data and compared to available field and laboratory BCF data. Wide variability was observed between the methods for estimating log K~ow~ and log D values, ranging up to several log units for particular congeners, and which was further compounded by additional variability introduced by the different BCF equations applied. With the exception of n-perfluorooctanecarboxylic acid (n-PFOA), whose experimental BCF was poorly modeled by all approaches, the experimental BCF values of the other PFSA and PFCA congeners were reasonably approximated by the ALOGPS log P values in combination with any of the five log K~ow~ based BCF equations. The SPARC and KOWWIN log K~ow~ and log D values provided generally less accurate BCF estimates regardless of the BCF equation applied. However, the SPARC K~ow~ values did provide BCF estimates for PFSA congeners with errors <0.3 log units using any of the five BCF equations. Model lipophilic and proteinophilic solvent based distribution constant calculations for the PFSA and PFCA congeners with experimental BCFs exhibited similar relationships with their corresponding BCF values. For longer chain PFCA and PFSA congeners, increasing hydrophobicity of the perfluoroalkyl chain appears to be driving corresponding increases in BCF values. Perfluorooalkyl sulfonamides are expected to display similar chain length and branching pattern influences on BCFs, but no experimental data are currently available upon which to validate the estimated values which range widely between the various approaches by up to 10 log units. The amidic proton acidity on primary and secondary perfluoroalkyl sulfonamides will play a significant role in the partitioning of these compounds with both abiotic and biotic organic matter, and will need to be taken into account when assessing their environmental and biological fate