An experimental customer satisfaction index to evaluate the performance of city logistics services

© 2016 Vilnius Gediminas Technical University (VGTU) Press. Freight transport in urban areas entails benefits (i.e. free access to goods when needed), but also negative externalities (environmental, social and transportation impacts). In response to these problems, the concept of city logistics emerged, for the purpose of planning, organizing, coordinating and controlling physical and information flows in order to find a compromise between efficient freight distribution in urban areas and protection of the environment. A typical city logistics initiative is the Urban Freight Consolidation Centre (UFCC), the benefits of which are significant. Its financial issues though represent a huge problem for public administrations. However, a large customer network, comprising retailers participating in the initiative, could make the UFCC a self-financing scheme. The key to expanding the scheme is closely linked with marketing campaigns and customer care. Therefore, customer care analysis represents an important tool in developing UFCC schemes. In this paper, a new Customer Satisfaction Index (CSI) is proposed for evaluating UFCC service quality. The new index, named CSImod, is a modified version of the traditional CSI, but places greater emphasis on customer dissatisfaction, so as to analyse the most critical areas of the service with a view to improving them. The index has been tested using experimental data collected within the CIVITAS RENAISSANCE Project, in which the Bristol and Bath Freight Consolidation Centre (BBFCC) scheme was evaluated. The evaluation was done from a user perspective, i.e. the participating retailers. The CSImod places more importance on the most dissatisfied customers making it possible to understand why they are dissatisfied and with what. Thus, it is possible to intervene with the aim of improving those areas of the service that are perceived as the worst. In spite of the high level of satisfaction with the overall service provided by the BBFCC, thanks to the CSImod the analysis pointed out that some retailers are dissatisfied with the delivery time arrangements and also with deliveries that were getting wet, issues about which the BBFCC manager was totally unaware. The CSImod could be used by UFCC operators to extend the network of the retailers involved and could therefore provide an implicit solution for making the scheme self-financing

Game Based Learning on Urban Sustainability: The "Sustain" Project

SUSTAIN is an ERASMUS+ project with an innovative perspective on urban sustainability. Its target is to promote the importance of sustainability on the everyday problems of the cities among the students of higher education, which are the policy makers of tomorrow and the ones that will shape the future. In order to achieve its goals, the research team will develop a course that will be based on an interactive game with an analytical style of education. This game will allow students to learn about transportation sustainability and societal metabolism through playing. In addition, the research team will develop small and illustrative simulation models, which will make the definitions more concrete and allow students to experiment in a consequence-free environment. It is a quite innovative and hybrid perspective way of learning, in the sense that it will combine game-based learning with a cognitive and analytical style of education

Spatio-Temporal Causal Relations at Urban Road Networks; Granger Causality Based Networks as an Insight to Urban Traffic Dynamics

Being spatio-temporal variations of urban road traffic a critical information for understanding and predicting accurately traffic, the current paper focuses on urban road traffic dynamics understanding by introducing the notion of causality. Using 15-min aggregated travel time series from taxi GPS data, causal networks are developed. The results reveal potentials arising from mining causality beyond correlation notion among urban road paths as well as the contribution of causal networks to a decision support system for traffic management. The knowledge on causal relations and the characteristic time lags on the ‘transfer’ of the information (traffic) among the road paths is a key knowledge for traffic management, since it gives the possibility to proactively intervene in the affected road paths and to inform users for alternative routes. Being high the extendibility and transferability potentials of the proposed approach, exploitation in other transport-related problems appears promising. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd

Representativeness of Taxi GPSEnabled Travel Time Data Using Gamma Generalized Linear Model

The sensor-era has brought rapid changes in transportation; the abundance of data has started changing the traditional way in which planners and engineers approach mobility. Nowadays, traffic monitoring and information provision systems heavily rely on floating car data usually of special vehicles (e.g., trucks, taxi), and the question that arises is whether such sources can provide reliable data for the whole traffic in a complex urban environment. The current paper, through Thessaloniki's (GR) case study, seeks to evaluate the reliability of taxi data compared to the overall traffic. The analysis reveals that for the examined critical urban road paths, there is a strong relation among floating taxi data with the overall traffic that is additionally influenced by other significant factors (e.g., number of lanes, day, time period). Furthermore, a modelling approach with a generalized linear model (gamma with log link) seems appropriate when dealing with skewed and heteroscedastic traffic data. Copyright © 2021, IGI Global

Clustering of Urban Road Paths; Identifying the Optimal Set of Linear and Nonlinear Clustering Features

Urban traffic is undoubtedly a dynamic phenomenon presenting variations over both time and space, that in the majority of cases are the result of a mixture of, either well known (i.e. weather, seasonality) or not easily predictable (i.e. events, accidents) external factors. Identification of similarities in the performance of different urban road paths under different traffic states (different travel demand conditions) is the main subject of the current paper. Floating taxi travel time data (timeseries per road path) collected in the framework of Thessaloniki Smart Mobility Living Lab (initiated and operated by CERTH/HIT) consist the basic input for the hierarchical clustering that is applied. Clustering applies upon different combinations of road paths’ features (data points of travel time timeseries, descriptive statistics and mutual information of timeseries). The comparison of the clustering results based on average weekdays travel times per road path (from a six months period) with the respective results of a typical and an atypical day adds on the interpretability of underlying relations among paths under different states. The analysis reveals that resulting clusters can be a building block for the spatiotemporal understanding of urban traffic. Furthermore, it is shown that adding as clustering feature the criterion of mutual information of timeseries, therefore taking into account also non-linear dependences of the different road paths, the clustering interpretability is differentiated. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

Exploiting the Knowledge of Dynamics, Correlations and Causalities in the Performance of Different Road Paths for Enhancing Urban Transport Management

The great abundance of multi-sensor traffic data (traditional traffic data sources - loops, cameras and radars accompanied or even replaced by the most recent - Bluetooth detectors, GPS enabled floating car data) although offering the chance to exploit Big Data advantages in traffic planning, management and monitoring, has also opened the debate on data cleaning, fusion and interpretation techniques. The current paper concentrates on floating taxi data in the case of a Greek city, Thessaloniki city, and proposes the use of advanced spatiotemporal dynamics identification techniques among urban road paths for gaining a deep understanding of complex relations among them. The visualizations deriving from the advanced time series analysis proposed (hereinafter referred also as knowledge graphs) facilitate the understanding of the relations and the potential future reactions/outcomes of urban traffic management and calming interventions, enhances communication potentials (useful and consumable by any target group) and therefore add on the acceptability and effectiveness of decision making. The paper concludes in the proposal of an abstract Decision Support System to forecast, predict or potentially preempt any negative outcomes that could come from not looking directly to long datasets. © 2019, Springer Nature Switzerland AG