User Perception towards Ride hail Service: A case of Nagpur city, India

In recent times, there is an increase in the utilization of the ride-hail service (viz., mobile application-based shared service) by the younger generation due to tremendous changes in technology, extensive usage of smartphones, and perceived convenience during travelling. For evaluating ride-hail service further, it is necessary to understand the user perception towards the ride-hail service and public transportation system (viz., bus in this case) to efficiently plan the public transportation services. Hence, the present study analyzes user perception while making trips, using ride-hail service and public transport buses. A long stretch of 3.8 km has been selected in the Nagpur city for the study and the stretch attracts a good number of trips by ride-hailing and public transportation. To perform the analysis, a survey has been conducted in the stretch which includes an online survey (viz., based on the Google form) and an offline survey (viz., at selected bus stops on the route between university campus and shopping complex). A binary logit model has been developed to understand the user perception towards ride-hail service and to identify the significant contributing factors towards the preference of the ride-hail service over the public transport bus. From the model results, it has been noted that age, accessibility, waiting time, travel time, income, and travel cost of the trip are the most significant contributing factors that affect user decision for availing the ride-hail service. It has been concluded from the study that the inferences obtained would be useful in the direction of developing warrants for transport planners and policymakers to improve the service quality of public transport buses (viz., frequency and routes) in cities especially in the Indian context.

Threshold value estimation of journey-distance using generalized polynomial function

The present work demonstrates an experience in estimating the threshold value of journey distances travelled by transit passengers using generalized polynomial function. The threshold value of journey distances may be defined as that distance beyond which passengers might no more be interested to travel by their reported mode. A knowledge on this threshold value is realized to be useful to limit the upper-most slab of transit fare, while preparing of a length-based fare matrix table. Theoretically, the threshold value can be obtained at that point on the cumulative frequency distribution (CFD) curve of journey distances at which the maximum rate of change of the slope of curve occurs. In this work, the CFD curve of the journey distance values is empirically modelled using Newton’s Polynomial Interpolation method, which helps to overcome various challenges usually encountered while an assumption of a theoretical probability distribution is considered a priori for the CFD.

Road Safety Resource Allocation Using Interactive Multiobjective Optimization

A model is proposed for allocating safety resources to various hazard sites. Due to budget constraints, allocation of resources for necessary countermeasures is a critical issue in safety improvement programs. Therefore, the Decision Maker needs a tool that can prioritize the identified countermeasures looking at several objectives, the most important of which are: reducing the number of accidents and minimizing the costs. A number of countermeasures could be implemented simultaneously in the same location and this was considered, so that the solution that best optimizes the objectives was selected. Since the considered objectives are not commensurable, a new methodology with interactive multi-objective optimization in the case of 0-1 integer variables was proposed, based on the application of a logical preference model built using dominance-based Rough Set Approach (IMO-DRSA). Finally, an application of the methodology is presented considering a sample of Italian urban intersections and a set of mutually exclusive alternatives at each location.

Designing in-car emotion-aware automation

Driver behaviour recognition is of paramount importance for in-car automation assistance. It is widely recognized that not only attentional states, but also emotional ones have an impact on the safety of the driving behaviour. This research work proposes an emotion-aware in-car architecture where it is possible to adapt driver’s emotions to the vehicle dynamics, investigating the correlations between negative emotional states and driving performances, and suggesting a system to regulate the driver’s engagement through a unique user experience (e.g. using music, LED lighting) in the car cabin. The relationship between altered emotional states induced through auditory stimuli and vehicle dynamics is investigated in a driving simulator. The results confirm the need for both types of information to improve the robustness of the driver state recognition function and open up the possibility that auditory stimuli can modify driving performance somehow.

Modeling of BRT System Travel Time Prediction Using AVL Data and ANN Approach

Improving the quality of public transportation systems and encouraging passengers to use them are effective solutions for reducing transportation problems in metropolitan. Prediction of travel time and providing information to passengers are significant factors in this process. In this research not only the travel time components in Bus Rapid Transit (BRT) system were investigated but also an Artificial Neural Network (ANN) model and a regression model for travel time prediction were presented. To enhance this aim, data was collected by AVL data and field observation and after investigating the primary independent variables, the significant ones were determined using statistical analysis, then ANN development was done. Moreover, linear regression method was used for this purpose. The results prove that although both models have high level of prediction accuracy, ANN model outperform the regression model and the accuracy for the route sections with no signalized intersections is higher than the others.

Sub-activity site selection and activity choice modelling in planned special events

The purpose of the study is to evaluate the impacts of the variables on site selection decision of the spectators just before the main activity in order to engage in eating, having fun and performing other types of activities. A multinomial logit modelling framework is hired to model activity patterns within PSE circumstances. Activities were classified into three groups that are “Eating”, “Entertainment”, and “Other”. Model estimation on PSE survey data set from selected stadiums in Istanbul shows that due to the congestion, as travel time and activity duration increase the spectators inclined to be around the stadium 184 minutes in average before the starting time of the main activity. The results obtained from this study can be used as a micro input for the macro studies such as transportation master plans and urban plans and can offer complementary research areas for PSE traffic management and urban planning.

Measuring costs of noise pollution generated by freight transport: Case of the Slovak and Czech Republic

The paper focuses on measuring and quantification of the negative externality of noise pollution generated by freight transport in the Slovak Republic and the Czech Republic. The paper describes negative impacts and significance of noise externalities, whereas it is established that noise causes psychological and physiological harm to affected persons. A separate part of the paper is dedicated to the current status of the European legislation dealing with the issues of the negative externality of noise pollution, in particular Directive 2002/49/EC of the European Parliament and of the Council and Communication COM(2008) 435. The actual measurement of the total, average and marginal costs of noise pollution is implemented in line with the defined methodology and using expert studies defined in the paper. The measurement results show that the costs of the negative externality of noise pollution are high in both countries. According to authors’ calculations, the total costs of the negative externality of noise pollution amount to EUR 100.8 mil in the Czech Republic and EUR 16.9 mil in the Slovak Republic. The paper contains a proposal of internalisation of these costs in the form of performance charges applied to operation of heavy goods vehicles.

Tourism and intraregional transport: The assimilation of local transport in tourist destination – the perception of Lisbon’s residents

This article aims to analyze the importance that scientific research has given to tourists’ transport, understand the perception of visitors on the adequacy of intraregional transport in the visited destinations, understand the value different type of transports assume in tourist visits, identify the most used means of locomotion in a tourist destination and evaluate the acceptance levels of a tourist transport circuit organized with crossing points at the main touristic attractions, where visitors can leave their vehicles in strategically located car depots. In this case study, 226 valid respondents were considered. The main conclusion of this research is that Portuguese visitors do not consider adequate the supply of public and collective transportation in the national destinations they visit. Not only, most travellers do not use this type of transport to reach a travel destination as, once there, they won’t use it. Different factors influence the choice of the travel transportation but if the destination provides efficient supply of collective transportation or environmental vehicles for individual use, visitors are willing to drop their vehicles in car deposits outside touristic centres.

Fitting Volume Delay Functions under interrupted and uninterrupted flow conditions at Greek urban roads

This paper presents the calibration of the most commonly used Volume Delay Functions (VDF): BPR, Conical, Akcelik and Modified Davidson, for an urban area populated by over 1 million inhabitants, the city of Thessaloniki in Greece. The estimation of the unknown coefficients was carried out for a typical freeway, the ring road of the city, and selected arterial and collected roads of the city center, through recent data of hourly observed vehicle speeds and volumes obtained from video recordings and loop detectors. The BPR function yielded the highest accuracy across all the examined road sections and was characterized as the most suitable to simulate and interpret the existing traffic conditions. The estimated coefficients differed significantly from the values proposed in the pertinent literature, which highlights the importance of using locally derived data for the calibration of the VDFs.

Support vector machines and k-means to build implementation areas of bundling hubs

City transportation has three basic components that create the essential environment for its functioning and the social welfare namely infrastructure, operational assets, and management policies. The key focus of this article is on understanding long-term distribution of transport demand in order to build bundling networks. To achieve this aim, we provide a hybrid machine-learning approach using a combination of several clustering and forecasting algorithms that are considered efficient given the key performance indicators obtained. This approach involves combining two types of algorithms: clustering and prediction algorithms. Based on simulated benchmarks, results indicated that the clustering phase is still appropriate using the k-means algorithm. To improve the k-means results, we measured 30 validation indices to estimate the number of clusters. In so doing, not only does it want to validate the clusters but also to identify the optimal k. To evaluate forecast accuracy in the demand prediction phase, we used the standard key performance indicators, namely MSE, RMSE, MAPE and R². The SVM algorithm has been judged as the most efficient prediction algorithm based on average values of the obtained metrics.