Abstract

     Based on the scores and trends of Sustainable Development Goals, Iran, our case study, has been unsuccessful in nearly all the pillars of sustainable development despite the variations in socioeconomic variables related to the SDGs. This implies a comprehensive underdevelopment in Iran, necessitating the improvement of infrastructure which significantly affects all the pillars of sustainable development including environment, health, and economy. This study, takes transportation as an important infrastructure which has various direct and indirect impacts on all pillars of sustainable development, especially in the case study of Iran. To evaluate this, the present study aims to investigate 1) which sustainability perspective dominates in Iran (strong sustainability, weak sustainability, or health-centered sustainability); and 2) the consistency degree of each transportation mode with each pillar of sustainability including environment, health, and economy. We employ VAR and simultaneous equations system to find which perspective of sustainable development dominates Iran; and to rank the transportations modes in accordance with their consistency with sustainable development in Iran during 1979–2016. Overall, the results of this study is found to be significant and support the strong sustainability perspective in Iran. The most compliant transportation modes with the sustainable development in Iran are then ranked as: a) rail, b) air, c) road, and d) maritime transportation. These results suggest the policy-makers to: 1) value the environmental issues at the top of the agenda for the sustainability strategies; and 2) to shift the load of passenger and goods to the rail transportation by investing and developing the rail network.

Introduction

     Starting from the industrial revolution, the vast increase in the production of consumer goods, as well as urbanization has been taking place all around the world. Although such development has had a positive impact on the livelihood of societies, its social reliance and impact on natural resources has increased considerable concerns on this transition. According to the latest data published by the World Bank, the world’s total population has already passed 7.76 billion, and is expected to reach over 9.7 billion by 2050 (World Bank, 2022). Such large population require an ever-increasing dependence on natural resources and the environment [70]. Having foreseen such sustained growth in population, the United Nations (UN) has been one of first intergovernmental entities to organize a series of comprehensive actions over the last few decades to incorporate sustainable means in this socio-technological development. Agenda 21, for instance, has been firstly introduced by the UN in 1992, as a comprehensive plan of actions to enhance the human lives and conserve the environment at the Earth summit in Rio de Janeiro. Later in 2015, the UN published a set of 17 goals, commonly referred to as Sustainable Development Goals (SDGs or also agenda 2030) which outline the objectives necessary to end poverty, inequality, environmental degradation, as well as advocating peace and justice [46,66]. Since then, the doctrine of global sustainability has advanced and become a key element of international sustainability studies and developmental agendas [13,56]. Following this, numerous studies have used the 17 set of goals as a playground to incorporate sustainability in various fields and industries.

Conclusion

     Based on the score of Sustainable Development Goals (SDGs), Iran has been unsuccessful in almost all of the sustainable development goals and pillars. This implies a comprehensive underdevelopment in Iran in various dimensions including environment, social, and economic. To evaluate this, the present study focuses on transportation, since it plays a key role in each single sustainable development pillar in Iran.

     This research tries to find the most consistent transportation mode with the sustainable development pillars in Iran. To find a more accurate ranking of transportation modes, we investigate which perspective of sustainable development dominates in Iran by producing and using HEY model with VAR approach based on World Bank data during 1979–2016. The findings point that the strong sustainability perspective is most effective in case of Iran which implies that the environment plays the most important role in sustainable development, before the social and economic pillars.

Abstract

     While electrification of road transport is a key component of decarbonisation, the implications for the broader economy and related jobs remain underexplored. We quantify these impacts in the EU in a global Computable General Equilibrium (CGE) model, combining techno-economic assumptions about electric vehicles with deployment scenarios derived by energy models. We augment input-output tables underlying the JRC-GEM-E3 model with an explicit representation of vehicle manufacturing and upgrade the modelling of vehicle purchase and operation. Our findings illustrate that greater road transport electrification reduces the overall costs of climate mitigation, primarily driven by lower fuel costs for electric vehicles and a faster decline of battery costs. Transport electrification alters supply-chains and leads to structural shifts in employment from traditional vehicle manufacturing towards battery production, electricity supply and related investments. Finally, we expand the set of labour market indicators to cover skills and occupations, to refine the socio-economic assessments of climate policy.

Introduction

     Global CO2 emissions from transport have continued to rise in recent years across all modes (IEA, 2020a). In the EU, transport greenhouse gas (GHG) emissions were 33 % higher in 2019 compared to 1990 levels, while other sectors achieved emissions reductions over the same period (e.g. energy supply −39 %, industry −35 %, EEA, 2020). This is in stark contrast with the EU’s objectives of 55 % emission reductions by 2030 compared to 1990 and to achieve net zero emissions by 2050.1

     Road transport, accounting for nearly one-fifth of total EU GHG emissions, has therefore become a priority sector for climate action and a key element of the European Green Deal (European Commission, 2019). To encourage rapid emissions reductions in road transport, Europe has recently proposed new initiatives for stronger emissions standards for cars and vans, the set-up of a new emission trading scheme for buildings and road transport, as well as new regulation for alternative fuel and charging infrastructure (European Commission, 2021, European Commission, 2021).

Conclusions and policy implications

     The electrification of road transport is accelerating across the globe, with significant increases in electric vehicle market shares in China, Europe and the United States in particular (IEA, 2020c). While long-term projections on the number of EVs in the car and truck fleets vary across models and methodologies, the technology is widely expected to make up the majority of the total vehicle stock by mid-century, as a result of increasingly stringent climate policies and technology advancements, notably in batteries. Such fleet transformation in the coming three decades will be associated with large shifts in the manufacturing of motor vehicles and their supply-chain in the near-term, as well as changes in the operation of vehicles (fuel shifting, energy efficiency, maintenance schedules, etc.).

     This paper explores the macroeconomic implications of road transport electrification as a contributing policy to reaching Europe’s climate targets. We combine techno-economic assumptions about the composition and operation of the fleet with fuel consumption projections from energy system models into a set of climate mitigation scenarios with varying degrees of road transport electrification. We run these scenarios in an augmented version of the JRC-GEM-E3 model, which explicitly represents the manufacturing of BEVs as a separate economic activity from the manufacturing of conventional ICE vehicles. This new methodology allows for a more detailed analysis of macroeconomic impacts in two major ways: first the supply chain implications of the deployment of EVs can be more clearly identified than if all vehicles were produced using a single input structure. This has large implications in terms of employment, and therefore on occupations and skills. Second, this also allows for a new decomposition analysis, where the various channels of impacts from EVs on macroeconomic results can be explored. Our modelling suggests that a higher degree of road transport electrification can contribute to reducing the costs of climate mitigation policies.

Abstract

     Vertical displacement is a common concrete slab sidewalk deficiency, which may cause trip hazards and reduce wheelchair accessibility. This paper presents an automatic approach for trip hazard detection and mapping based on deep learning. A low-cost mobile LiDAR scanner was used to obtain full-width as-is conditions of sidewalks, after which a method was developed to convert the scanned 3D point clouds into 2D RGB orthoimages and elevation images. Then, a deep learning-based model was developed for pixelwise segmentation of concrete slab joints. Algorithms were developed to extract different types of joints of straight and curved sidewalks from the segmented images. Vertical displacement was evaluated by measuring elevation differences of adjacent concrete slab edges parallel to the boundaries of joints, based on which potential trip hazards were identified. In the end, the detected trip hazards and normal sidewalk joints were geo-visualized with specific information on Web GIS. Experiments demonstrated the proposed approach performed well for segmenting joints from images, with a highest segmentation IoU (Intersection over Union) of 0.88, and achieved similar results compared with manual assessment for detecting and mapping trip hazards but with a higher efficiency. The developed approach is cost- and time-effective, which is expected to enhance sidewalk assessment and improve sidewalk safety for the general public.

Introduction

     Public sidewalks are essential infrastrstructures in cities to provide convenience for urban life. Deficiencies of sidewalks will lead to inconvinence, disruptions and potential hazards to residents. Hence, it is important to monitor and evaluate sidewalk condition such as to take necessary maintenance measures to ensure the normal functionality of sidewalks. To ensure public sidewalks remain in good conditions, local governments usually have their own sidewalk program to assist private property owners (who are the maintaining authority of the sidewalk adjacent to their property) with concrete slab evaluation and defect correction. The typical traditional approach for sidewalk surveying is using smart-level and measuring tools, e.g. tapes, to manually take slope readings and evaluating the compliance with related regulations. However, such manual surveying method takes a long time to assess overall conditions of sidewalks, for example, the City of Middleton and the Village of Shorewood both require eight years to go through each neighborhood (City of Middleton, 2021b, Village of Shorewood, 2021).

Conclusion

     This paper developed and tested a sidewalk trip hazard detection and geo-visualization method that can automatically assess concrete slab deficiencies after obtaining the point clouds via a low-cost LiDAR scanner. Firstly, low-cost mobile LiDAR devices were used to scan sidewalks to obtain the point cloud data, which were then converted to RGB images using the develop tool. Second, a deep learning-based segmentation model U-Net was trained with the sidewalk images to segment concrete joints in the image. Afterwards, joints were extracted from the segmented image and vertical displacements for each joint were evaluated, based on which potential trip hazards were identified and specific information was geo-visualized in Web GIS platform. The experiment results demonstrated the effectiveness of the proposed method. Specifically, the segmentation model performed well for segmenting different types of joints in images (with a highest joint IoU of 0.88) and all the vertical displacement conditions were accurately and comprehensively detected. It was found that integrating the RGB feature with the Normal feature can improve the joint segmentation accuracy of the deep learning model, but the improvement was not significant. For future application, using the point cloud converted orthoimages is sufficient to detect joints. In this study, the segmentation model trained with a few images of straight sidewalks with groover cut contraction (control) joints and the corresponding joint label images already obtained good performance, but adding extra images, such as vegetation covered joints, to enrich the dataset will be considered for future application. Compared to the methods (in Table 2) in existing studies, scanning the as-is condition of the sidewalk with a mobile device is convenient and faster in achieving full-width coverage.

Abstract

Keywords

1. Introduction

2. Literature review

2.1. Employee-based brand equity

2.2. Customer relationship management dimensions

2.2.1. Customer orientation

2.2.2. Knowledge management

2.2.3. CRM organization

2.2.4. Technology based CRM

3. Methodology

4. Analysis of results

4.1. Common method Bias

4.2. Descriptive statistics

4.3. Measurement model

4.4. Structural model

5. Discussion and conclusion

6. Limitations and future research

Appendix A. Factor loadings of remaining items based on CFA results

References

Abstract

     The purpose of this study was to explore the effect of customer relationship management (CRM) dimensions on employee-based brand equity. By looking at the past literature, it can be noticed that the majority of earlier studies on brand equity examined it from customers’ perspective, while the employees’ perspective received less emphasis. Thus, the required data was obtained using a survey tool from 164 employees in the ride-hailing industry in Saudi Arabia. The obtained data was analyzed by the Covariance-Based Structural Equation Modeling (CB-SEM) method to test the hypothesis and reach at conclusions. The findings showed that CRM in general has a positive effect on employee-based brand equity. Specifically, it was found that CRM organization has a significant positive impact on employee-based brand equity. It was also verified that knowledge management and customer orientation have positive effects on overall employee-based brand equity. Finally, the results confirmed that technology-based CRM has a significant positive impact on employee-based brand equity. These results add to the body of literature on this topic and verify the significance of customer relationship management dimensions in affecting employee-based brand equity.

Introduction

     The topic of brand equity has received a noteworthy attention from several scholars since the last decades. The examination of brand equity has mainly been centered towards determining the factors that could improve brand value (Dinçer, Bozaykut-Buk, Emir, Yuksel, & Ashill, 2019). Although the concept has many definitions in the previous literature, there is a universal agreement that brand equity symbolizes the tangible and intangible value of a firm’s products or services that are devised from the unique name of the brand (Srivastava & Shocker, 1991). A strong brand equity provides noteworthy implications to firms, their clients, and to the investors as well. It enables the service providers to generates several benefits, such as: charging higher prices than the rivals for similar products, maintaining business customers on the long term even though competitors peruse price cutting strategies, and withstanding negative information about the failure of a certain product or service (Gelb & Rangarajan, 2014). High brand equity also permits firms to use the same brand name when they successfully introduce a new product. As the competition has increased recently among businesses and due to the imitation of a brand’s offerings, the acquisition of a reliable and trustworthy brand name has been regarded as the key distinguishing element. According to Augusto and Torres (2018); and Phung, Ly, and Nguyen (2019), a firm that acquires high brand equity tends to have greater trust from its consumers. Moreover, Poulis and Wisker (2016) tested the effect of employee-based brand equity on organizational performance and found that the attachment of employees to a brand represents the main factor that contributes to better financial outcomes.

Results and analyses

     As stated above, the collected data were analyzed using the CB-SEM approach. In order to check the likely existence of common-method variance bias among constructs, this paper relied on the CFA and single factor test of Harman (1960), and full collinearity test of Kock and Lynn’s (2012). When all of the measurement items of the constructs were constrained to be loaded on one single common factor, the overall variance explained by a general latent variable was lower than the Harmin’s threshold value of 50%, indicating that the final model is considered free from common method bias. Additionally, all of the Variance Inflation Factors (VIFs) in a full collinearity test were less than 5, suggesting that there is no indication of existing pathological collinearity (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

Abstract

Keywords

References

Abstract

Connected vehicles can transmit real-time information to traffic control management systems. Despite the recent technical advances of telecommunication networks and mobile computing there have been no real-time adaptive traffic signal control experiments with connected vehicles. Most of the research in this field has been carried out only with simulations. In this work we present the computer system that was adopted to regulate traffic signals in real-time with “smartphone-connected” vehicles as the only source of information. We introduce the description of the computer system architecture that was deployed in an experiment of a Floating Car Data (FCD)-based adaptive traffic signal in which a traffic signal has been regulated in real-time with 100% “smartphone-connected” vehicles. The description of the system based on commonly-used technologies could help others to develop and deploy new traffic signal management systems in new “connected” intersections.

1. Introduction

Both connected vehicles and connected traffic signals are poisoned to become an important part of the forthcoming Internet of Things (IoT) and of Intelligent Transportation Systems (ITS). Connected vehicles may help to manage and control road traffic in many new ways. Classic road traffic engineering practice was founded on efforts to allocate demand on transit systems [1] and on better road traffic control by adopting tools such as: traffic simulation [2–7] dynamic network loading equilibrium and dynamic models [8–11] and the implementation of efforts to influence user route choice [12–16]. There have been no practical advances in traffic signal regulation in the last years and traffic signals are very often controlled with prefixed signal settings that are not always appropriate for the always-changing traffic flows. This can cause traffic congestion that is a serious problem in cities and also a great cause of air pollution. The use of inductive loops for real-time traffic signal control is the current standard and its limitation lies in the expensive infrastructure and maintenance resources that are necessary to keep them working. Existing technologies such as smartphones can contribute to creating cheap “smartphone-connected” vehicles that can act as floating probe vehicles [17,18] (Floating Car Data: FCD), or the use of dedicated local wireless networks can create a dedicated vehicle to infrastructure (V2I) communication systems. By combining smart-phones with existing wireless mobile phone data networks and Satellite localization systems, ITS can be deployed at a very low cost compared with ad hoc developed wireless systems that would bring higher costs. The first research efforts on smart-phone-based FCD did not benefit from satellite positioning systems, nowadays, mobile phones can obtain position data and transmit this information; thus it is possible to evaluate travel times and speeds [19,20]. An assessment of some safety issues is also possible [21]. with assistance for a safer driving [22]. The estimation of fuel consumption [23–26], the measurement of road pavement problems [27,28], and route choice behavior-based research are also possible with smart-phones.

Highlights

Abstract

Keywords

1. Introduction

2. Literature review

3. Problem formulation

4. Methodology

5. Computational experiments

6. Conclusions

CRediT authorship contribution statement

Acknowledgments

References

Abstract

The Bike Repositioning Problem (BRP) has raised many researchers’ attention in recent years to improve the service quality of Bike Sharing Systems (BSSs). It is mainly about designing the routes and loading instructions for the vehicles to transfer bikes among stations in order to achieve a desirable state. This study tackles a static green BRP that aims to minimize the CO2 emissions of the repositioning vehicle besides achieving the target inventory level at stations as much as possible within the time budget. Two types of bikes are considered, including usable and broken bikes. The Enhanced Artificial Bee Colony (EABC) algorithm is adopted to generate the vehicle route. Two methods, namely heuristic and exact methods, are proposed and incorporated into the EABC algorithm to compute the loading/unloading quantities at each stop. Computational experiments were conducted on the real-world instances having 10–300 stations. The results indicate that the proposed solution methodology that relies on the heuristic loading method can provide optimal solutions for small instances. For large-scale instances, it can produce better feasible solutions than two benchmark methodologies in the literature.

1. Introduction

In Bike Sharing Systems (BSSs), people can rent shared bikes to satisfy the needs for short-distance travel, such as the connection between home and a subway station, or just a leisure trip. However, due to the existence of asymmetric bike flows, some stations may be filled-in with returned bikes while some others are empty. For example, during the morning peak, the bike stations in residential areas might be empty, whereas those in the business area might be full. In a hilly area, the stations at the bottom can be full whereas those at the top can be empty because downhill trips are usually more than uphill trips. Therefore, the bikes need to be redistributed among all the stations after a certain period, so that a desirable inventory level can be maintained at each station to satisfy upcoming users’ demand. This operational problem is referred to as the Bike Repositioning Problem (BRP).

The repositioning operation is mainly about using a dedicated fleet of trucks or trailers to transport bikes among stations in order to reach the expected inventory level at each station (Raviv et al., 2013). There are generally two types of repositioning: static and dynamic. For the static repositioning that most of the current studies focus on (e.g., Cruz et al., 2017, Ho and Szeto, 2017, Liu et al., 2018, Szeto and Shui, 2018), the bike inventory and demand levels at each station are assumed to be relatively stable over time. The dynamic repositioning considers the variations of bike inventory and demand levels over time within a day (see Ghosh et al., 2017, Zhang et al., 2017, Shui and Szeto, 2018, Brinkmann et al., 2019). These two types of repositioning consider different application scenarios.

Due to the coronavirus global crisis, most countries have put in place restrictive measures in order to confine the pandemia and contain the number of casualties. Among the restrictive measures, air traffic suspension is certainly quite effective in reducing the mobility on the global scale in the short term but it also has high socioeconomic impact on the long and short term. The main focus of this study is to collect and prepare data on air passengers traffic worldwide with the scope of analyze the impact of travel ban on the aviation sector. Based on historical data from January 2010 till October 2019, a forecasting model is implemented in order to set a reference baseline. Making use of airplane movements extracted from online flight tracking platforms and online booking systems, this study presents also a first assessment of recent changes in flight activity around the world as a result of the COVID-19 pandemic. To study the effects of air travel ban on aviation and in turn its socio-economic, several scenarios are constructed based on past pandemic crisis and the observed flight volumes. It turns out that, according to these hypothetical scenarios, in the first Quarter of 2020 the impact of aviation losses could have negatively reduced World GDP by 0.02% to 0.12% according to the observed data and, in the worst case scenarios, at the end of 2020 the loss could be as high as 1.41–1.67% and job losses may reach the value of 25–30 millions. Focusing on EU27, the GDP loss may amount to 1.66–1.98% by the end of 2020 and the number of job losses from 4.2 to 5 millions in the worst case scenarios. Some countries will be more affected than others in the short run and most European airlines companies will suffer from the travel ban. We hope that these preliminary results may be of help for informed policy making design of exit strategies from this global crisis.

Introduction

The recent COVID-19 Coronavirus outbreak and the relevant precautionary measures to limit its spreading are having clear impacts on human mobility at global scale. This provoked a reduction of domestic and international volumes of air passenger traffic to and from China in February (Iacus et al., 2020). Such effects are currently being observed in several regions worldwide. This has clear implications for the aviation industry as well as indirect consequences to several sectors (e.g. tourism) and the economy at large as well as the society. Through the use of historical air traffic data, real time flights tracks and on-line booking systems, this works aims to provide air traffic volume projections following a set of scenarios base on observed and previous crises. These range from a rapid and full recovery to less optimistic scenarios of slower or even incomplete recovery which will depend on the duration and intensity of the lock-down. The trend of mobility at global scale has been rising over the last decade at a pace that is faster than the global world population growth (Recchi et al., 2019). Nevertheless, air traffic flows have been shaped at national and regional scale by shocks due to political instability, terrorism and economic crises (Gabrielli et al., 2019).

Abstract
Video transmission in Internet of Vehicles (IoVs) is an emerging technology which utilizes the multimedia inside and outside vehicles itself through Vehicle Ad Hoc Networks (VANETs). In IoVs, traditional multi-hop routing protocols are not adaptive to mobile environment, especially the high-mobility driving environment in which vehicles need to accomplish video transmission under high quality of service (QoS). In this paper, we propose a multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection (MRVT-CAS). We design a packet generation method for MRVT-CAS and use Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct the candidate set of next-hop selection. Then we map the expression of different genes in cell to selection of different next-hop nodes, and employ the mechanics of cellular attractor selection to select next-hop node. Moreover, we present a real-time feedback process to improve self-adaptability and robustness of routing protocol. Our simulation study compares MRVT-CAS with other routing protocols to evaluate performance of video transmission. The simulation results demonstrate the performance improvement over traditional methods, in terms of reachability, delay, stability and frame loss rate.

1- Introduction

2- The new model

3- Linear stability analysis

4- Nonlinear stability analysis

5- Simulations

6- Conclusions

References

Abstract

Advanced communications technology enables us to obtain the Headway Variation Tendency (HVT) of the next moment in a car following scenario. To maximize the benefits of the new technology, we developed a new traffic flow model that takes into consideration the effect of HVT. First, linear analysis method is used to derive the linear stability condition, that determines whether the HVT can improve traffic system stability. Then, using the reductive perturbation method, a modified KdV (mKdV) equation is obtained and the kink–antikink solutions are derived. The phase diagram of driver sensitivity against equilibrium headway is also plotted by using the neutral stability line, derived from linear analysis and the coexisting line, derived from nonlinear analysis. This diagram demonstrates that HVT does have the ability to improve the stability of traffic stream. Finally, the effect of HVT is validated through numerical simulations, where it is evident that the information of HVT can smooth the traffic stream and prevent the formation of traffic congestion.

Introduction

The growing traffic jam causes excessive fuel consumption as well as emissions to the urban environment. Such adverse impacts pose a severe threat to the traffic system performance. Various traffic flow models have been developed, to explore the mechanism of traffic jam formation [1–40]. Generally, traffic flow models are divided into macroscopic models based on fluid mechanics, mesoscopic models based on gas dynamics theory and microscopic models based on self-driven particles. The macroscopic models include continuum [1–5] and lattice hydrodynamic models [6–13]. The mesoscopic models [14], as a bridge between microscopic and macroscopic models, can capture the probabilistic nature of interactions between vehicles and describe the macroscopic characteristics of the traffic system. The microscopic models investigate the movement of the individual vehicle, and mainly include cellular automata [15–19], car-following models [20–28] and application of microscopic models in various scenarios [29–34].

1- Introduction

2- Related work

3- Methodology

4- Case study

5- Conclusions

References

Abstract

Civil infrastructure system maintenance planning is to determine which facility should be repaired, when and how maintenance should be carried out, and what treatment should be used under budget and other resource constraints. In the existing literature, various simulation and optimization models have been developed to help select the optimal maintenance plan. However, the developed models overlooked the deterioration propagation between adjacent connected facilities of the network infrastructure system. For instance, a facility receiving zero maintenance or having a failure of maintenance treatment affects not only the condition of itself, but also the deterioration rate of its neighboring facilities. This raises the call for taking the deterioration propagation into consideration when developing optimization models and capture to which extent it can affect the optimal maintenance plan. Therefore, in this paper, an infrastructure maintenance planning model considering the deterioration propagation between facilities is formulated as a mixed integer linear programming problem. A heuristic algorithm was proposed to solve the problem efficiently. Example networks were tested for the performance comparison between CPLEX and the heuristic algorithm. The proposed model performs better than models without the deterioration propagation.

Introduction

Civil infrastructure systems (e.g., roads, bridges, water supply, and wastewater) are exposed to aging effects and eventually subject to failure if no maintenance intervention is carried out. To prevent/delay failures, maintenance treatments need to be applied periodically. In order to optimize the allocation of resources for maintenance of the facilities, maintenance planning is needed. The primary objective of maintenance planning is to help decision makers schedule maintenance actions and determine which facility need to be maintained when maintenance should be carried out and which treatments should be used. As transportation infrastructure systems are spatially distributed assets covering large regions, they are specially characterized by the interdependence and interaction within and between different systems. Researchers have developed maintenance planning models addressing different relationships between facilities/systems from functional, economic, and other perspectives. For example, Bernhardt and McNeil (2004) stated that pavements are interconnected through geography, which implied the economies of scale in contracting long stretches of pavement for rehabilitation and the diseconomies of scale in terms of the disruption to users. Gao and Zhang (2013b) also pointed out that road sections selected for maintenance by traditional optimization approach are usually distributed spatially across the network. The authors suggested that, to take advantage of economies of scale, adjacent pavement sections with similar maintenance needs should be maintained within a single project. Rasmekomen and Parlikad (2013) conducted a study on optimizing maintenance plans for industrial assets by considering degradation and performance interaction between them.

Vehicle routing problem (VRP) research has recently improved dramatically to simulate more real-life circumstances. Nevertheless, the typical VRP models proposed have been isolated from the most important factor determining the success of the VRP plan on the ground, i.e. the human factor (driver). Thus, this research investigates the effect of drivers’ behaviours on the optimal VRP plan by integrating the level of autonomy of both planner and drivers as represented by risk-taking parameters. To enhance the model configuration’s practicability, the concept of ‘ridesharing’ e which has been introduced before e has also been integrated to expand the logistical services, improve customer satisfaction, and compensate for shortages in service. Moreover, to ensure environmentally-friendly logistical practices, a velocity maximisation policy and environmental penalty enforcement on the chosen velocity range have been considered. In general, the model improves drivers’ satisfaction, customers’ perceived quality, and the firm’s financial objectives. Additionally, it achieves a better supply chain strategic fit by planning at the three levels: strategic, tactical, and operational. A numerical example was solved using the Eclipse Java 2018-09 solver through two heuristic methods, the Greedy and the Intra-route neighborhood heuristic, and both revealed the same near-optimal solutions. Sensitivity analyses showed that the resulting insignificant increase in the VRP costs due to assigning autonomy for drivers is still reasonable, and the total costs’ objective function weight has an insignificant effect on the total near-optimal solution, while that of the energy consumption objective function has the largest impact.

Introduction

One of the most popular applications of operation research science is the VRP, which was defined by Clarke and Wright (1964) as serving customer networks distributed at different geographic points, using fleets of trucks from different capacities. VRP-related studies have exponentially grown by around 6% per year in the research literature (Eksioglu et al., 2009). Furthermore, VRP has grasped its importance due to its wide usage in logistic and transportation aspects. Moreover, the literature has concentrated on different variants related to VRP, in which scholars set models and solutions for many real-life problems to control difficulties associated with different stages of transportation, such as travel times, pick-up and delivery time windows, and information input (Braekers et al., 2016). Paraskevopoulos et al. (2017) stated that the routing and scheduling planning processes confront some challenges in allocating scarce resources for certain services. On the other hand, the VRP topic has been widely analysed due to its impact on various industries (Lahyani et al., 2017). Based on a taxonomic review related to the VRP topic during the period 2009e2015, it was revealed that the literature focused on some important VRP aspects, and different models were suggested to solve the problems of different objectives, including the capacitated VRP, periodic VRPs, the VRP with time windows, and others (Braekers et al., 2016). Additionally, a very crucial aspect that considerably affects the near-optimal results required for such a VRP case is augmenting the human factor in VRP models.

In the classical Vehicle Routing Problem (VRP), it is assumed that each worker moves using an individually assigned vehicle. Removing this core hypothesis opens the door for a brand new set of solutions, where workers are seen as transportable resources that can also move without the help of a vehicle. In this context, motivated by a major European energy provider, we consider a situation where workers can either walk or drive to reach a job and where carpooling is enabled. In order to quantify the potential benefits offered by this new framework, a dedicated Variable Neighborhood Search is proposed to efficiently tackle the underlying synchronization and precedence constraints that arise in this extension of the VRP. Considering a set of instances in an urban context, extensive computational experiments show that, despite conservative scenarios favoring car mobility, significant savings are achieved when compared to the solutions currently obtained by the involved company. This innovative formulation allows managers to reduce the size of the vehicle fleet while keeping the number of workers stable and, surprisingly, decreasing the overall driving distance simultaneously.

Introduction

Transportation in urban areas is increasingly facing new challenges. On the one hand, the systematic use of cars produces hazardous impacts on the environment, such as noise, toxic emissions, and the effects induced by greenhouse gases (Knörr, 2008). On the other hand, as highlighted by Jabali, Woensel, and de Kok (2012), city centers suffer from congestion and limited parking space. These phenomena, which are magnified by low vehicle occupancy rates, decrease the intrinsic efficiency of car-based transportation. Consequently, current legislation tends to constrain the use of cars within city centers either by limiting the number of authorized vehicles or completely banning vehicles in specific areas, such as pedestrian zones, as highlighted by Parragh and Cordeau (2017). For all these reasons, reducing the systematic use of cars in urban areas is becoming increasingly important. Firms that provide on-site services or parcel deliveries are directly concerned by these issues, as a substantial part of their activities takes place in metropolitan areas. We focus on the case of a large European energy provider, denoted by EEP (it cannot be named because of a non-disclosure agreement), that routes technicians to provide on-site services (e.g., small maintenance work, consumption evaluations, and consumer-setting upgrades). Every day, technicians who are not assigned to clients are employed for heavy works on the electricity network. However, once assigned to on-site services, the workers cannot be re-assigned thereafter to heavy works, even if they terminate their working day earlier.