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This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

The green logistics literature remains undecided on how collaboration between shippers (i.e. logistics buyers) and logistics service providers (LSPs) may facilitate green logistics practices (GLPs). This paper identifies two types of collaboration mechanisms, relation specific and knowledge sharing, to systematically examine their influence on facilitating the different types of GLPs – as seen by shippers versus LSPs.

Survey responses of 169 shippers and 162 LSPs in Sweden were collected and analysed using exploratory- and confirmatory factor analysis, followed by multiple regression analysis.

The findings reveal that neither of the actors consistently favour a certain type of collaboration mechanisms for facilitating all types of GLPs. Although it was found that both actors share the same view on the role of collaboration mechanisms for some GLPs, their views took contrasting forms for others.

This study contributes to the green logistics literature by incorporating a trilateral distinction to present collaboration recommendations for GLPs, based on (1) the collaboration mechanism at play, (2) the actor's perspective and (3) the GLP in question.

Insights are offered to managers at shipper/LSP firms to apply the right (“fit for purpose”) collaboration mechanisms in their relationships with their logistics partners with respect to the desired GLPs.

This is one of the first large-scale studies to systematically reveal in what way collaboration can facilitate the different types of GLPs.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

The purpose of this study is to measure the gaps between the engagements of shippers (i.e. logistics buyers) and logistics service providers (LSPs) in different green logistics practices (GLPs) throughout the key phases of the logistics purchasing process: request for proposal, negotiations, contracting and execution.

A large-scale survey of shippers and LSPs in Sweden was conducted. Respondents were 331 firms (169 shippers, 162 LSPs). Mean values of the actors' perceptions were analysed using independent- and paired sample t-tests.

While this study supports previous research indicating that LSPs engage more extensively in selling GLPs than shippers do in buying them, it shows that this conclusion does not uniformly apply to all GLPs nor all purchasing phases. Three patterns emerged for the gaps between the actors' buying-selling engagements throughout the purchasing process: (1) steady and wide gaps, (2) steady and narrow gaps and (3) emergent gaps. Distinct GLPs were associated with each pattern. It is also shown that the prioritisation of GLPs is fairly aligned between shippers and LSPs.

This study contributes to the green logistics purchasing literature by systematically and simultaneously creating three types of distinction, between (1) shippers and LSPs, (2) different GLPs and (3) different logistics purchasing phases. Future studies could replicate the analysis in countries other than Sweden.

Managers of shipper/LSP firms learn tips to spot the GLPs that their partners prioritise, enabling them to modify their purchasing/marketing strategies accordingly.

The three types of distinction represent a novel approach in the green logistics purchasing literature.

This chapter provides a reflective commentary on how the transition to electric vehicles (EVs) may alter how society uses cars through an inspection of evidence from the studies which have examined the impact of EV adoption on trip patterns. A framework for evaluating trip patterns is applied which considers how the adoption of an EV could generate impacts for the spatial distribution of car trips, when these trips occur, the journey purpose these trips serve, and the driving style in which the trips are conducted. It is identified that the principal issue which is likely to motivate alterations in trip patterns following a transition to EVs is the technical and regulatory differences which distinguish them from conventional vehicles. Spatial trip patterns could become anchored to the burgeoning chargepoint infrastructures, with network coverage having implications for where EVs will be seen. Changing seasons could reduce the range of the battery packs, limiting the useability of EVs in winter months. Low operating costs of EVs may encourage their use for short distance trips due to a feeling of guilt-free travel. Eco-driving functions of EVs could promote sustainable driving practices by gamifying energy efficiency though the introduction of targets, medals, and leader boards. It is concluded that the exact manner in which trip patterns will be altered by the transition to EVs is difficult to predict with clarity, with many alternative futures being conceivable. In part, the impact on trip patterns will be contingent on whether or not EVs start to look and feel like conventional cars as the technology matures.

The “most economically advantageous tender,” as defined in the EUʼs public procurement directives, allows public purchasers to combine environmental aspects, price and other award criteria in decision making. The directives do not, however, determine how the environmental criteria should be built. Indeed, there could be different means to assess the “greenness” of competing tenders, and these various measurements of environmental impacts may lead to different assessments of the most economically advantageous tender. In this article, the determination of environmental award criteria is examined through a case study on a purchase of a goods transportation service, where the most economically advantageous tender is calculated by life cycle assessment and the environmental cost calculation method suggested by the EU, and compared to the results gained by the purchaserʼs equation. Also the contribution of the weighting for the “green” purchasing decision is discussed.

Purpose – To examine the cost-effectiveness of UK government policy with respect to the mitigation of carbon emissions from the transport sector.

Methodology/approach – Existing policy as set out by the Department for Transport in Low Carbon Transport: A Greener Future is examined. This document elaborates a Low Carbon Transport Strategy intended to achieve annual emissions savings of 17.7 MtCO2 by 2020. A wide range of policy areas where further action could be taken to reduce carbon emissions are examined and their cost-effectiveness considered.

Findings – Measures that influence behaviour including smarter choices, eco-driving across modes, freight best practice and modest price increases are highly cost-effective. More cost-effective routes to saving 17.7 MtCO2 are identified, as are further cost-effective savings.

Originality/value – It appears that government targets could be delivered and indeed exceeded at lower cost than the Low Carbon Transport Strategy. However, policy development is influenced by a wide range of factors which help to explain why cost-effective measures are not always fully exploited.

Long haul freight transport imposes huge negative environmental externalities on society. Although these can never be entirely eliminated, they can be reduced. The purpose of this chapter is to analyse some of the many mitigating measures, or interventions, that can be used.

The approach used in this chapter is to review the literature and provide an overview of the main theoretical and practical mitigation measures available to transport operators.

There are literally thousands of possible mitigation measures and combinations that can be used by operators to reduce their environmental footprint. Each of these measures warrants a separate chapter. This chapter can only present an overview of the principle available measures. Although some mainland European examples are used, it is acknowledged that the examples used are somewhat skewed towards the United Kingdom.

The value of the chapter is in bringing together some of the many measures and approaches that can be used to reduce the environmental externalities of long haul freight transport. Much of the information on such interventions is based on industrial and EU project sources rather than purely academic research and so is less likely to be found in academic journals.