Emission Estimation of On-Demand Meal Delivery Services Using a Macroscopic Simulation.

While macroscopic simulations of passenger vehicle traffic within cities are now common practice, the integration of last mile delivery into a macroscopic simulation to evaluate the emissions has seldomly been achieved. In fact, studies focusing solely on last mile delivery generally focus on evaluating the delivery service itself. This ignores the effect the delivery service may have on the traffic flow in cities, and therefore, on the resulting emissions. This study fills this gap by presenting the results of two macroscopic traffic simulations of New York City (NYC) in PTV VISUM: (i) on-demand meal delivery services, where the emissions are evaluated for each OD-Pairs (i.e., each trip) and (ii) on-demand meal delivery services, where the emissions are evaluated for each link of the network (i.e., street). This study highlights the effect on-demand meal delivery has on the travelled distance (i.e., detours), congestion and emissions per km of every vehicle in the network, not just the delivery vehicles.

Land Efficient Mobility: Evaluation of Autonomous Last Mile Delivery Concepts in London.

Land efficient last mile delivery concepts are key to reducing the traffic in cities and to minimising its environmental impact. This paper proposes a decision support method that evaluates the autonomous delivery concept and applies it to one year's worth of real parcel delivery data in London. Deliveries to modular and fixed lockers with autonomous delivery vans and road-based autonomous lockers (RAL) and sidewalk autonomous delivery robots (SADRs) have been simulated. Various types of autonomous delivery van fleets, depot locations, customer modes of transport, parcel demand levels, parcel locker network densities and adjustment frequencies of modular lockers are considered. A routing and scheduling algorithm is used to optimise delivery tours and vehicle choice. The optimisation algorithm finds both the optimal number of collection and delivery points (CDPs) and the delivery concept (e.g., modular lockers, sidewalk autonomous delivery robot) depending on the customer mode chosen. The results show that modular lockers which are adjusted weekly are the best option for the current or higher parcel demand levels and road-autonomous parcel lockers (RAL-R) are the best option at the lowest parcel demand level.

Sensitivity Analysis of Emission Models of Parcel Lockers vs. Home Delivery Based on HBEFA.

Global concerns about the environmental effects (e.g., pollution, land use, noise) of last-mile deliveries are increasing. Parcel lockers are seen as an option to reduce these external effects of last-mile deliveries. The contributions of this paper are threefold: firstly, the research studies simulating the emissions caused by parcel delivery to lockers are summarized. Secondly, a demand model for parcel deliveries in New York City (NYC) is created for 365 days and delivery trips to lockers and homes are optimized for 20 "real-world" scenarios. Thirdly, using the emission factors included in the HandBook Emission Factors for Road Transport (HBEFA) database, the maximum percentage of customers who could pick up a parcel by car from parcel lockers that would result in fewer total emissions (driving customers + walking customers) than if home deliveries were adopted is calculated for various pollutants and scenario assumptions (i.e., street types, temperature, parking duration, level of service and vehicle drivetrain). This paper highlights how small changes in the calibration can significantly change the results and therefore using average values for emission factors or only considering one pollutant like most studies may not be appropriate.

Comparison of in-sight and handheld navigation devices toward supporting industry 4.0 supply chains: First and last mile deliveries at the human level.

Last (and First) mile deliveries are an increasingly important and costly component of supply chains especially those that require transport within city centres. With reduction in anticipated manufacturing and delivery timescales, logistics personnel are expected to identify the correct location (accurately) and supply the goods in appropriate condition (safe delivery). Moving towards more environmentally sustainable supply chains, the last/first mile of deliveries may be completed by a cyclist courier which could result in significant reductions in congestion and emissions in cities. In addition, the last metres of an increasing number of deliveries are completed on foot i.e. as a pedestrian. Although research into new technologies to support enhanced navigation capabilities is ongoing, the focus to date has been on technical implementations with limited studies addressing how information is perceived and actioned by a human courier. In the research reported in this paper a comparison study has been conducted with 24 participants evaluating two examples of state-of-the-art navigation aids to support accurate (right time and place) and safe (right condition) navigation. Participants completed 4 navigation tasks, 2 whilst cycling and 2 whilst walking. The navigation devices under investigation were a handheld display presenting a map and instructions and an in-sight monocular display presenting text and arrow instructions. Navigation was conducted in a real-world environment in which eye movements and device interaction were recorded using Tobii-Pro 2 eye tracking glasses. The results indicate that the handheld device provided better support for accurate navigation (right time and place), with longer but less frequent gaze interactions and higher perceived usability. The in-sight display supported improved situation awareness with a greater number of hazards acknowledged. The benefits and drawbacks of each device and use of visual navigation support tools are discussed.

A Consensus-Based Grouping Algorithm for Multi-agent Cooperative Task Allocation with Complex Requirements.

This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies. We address the problems of handling these challenges and improve the efficiency of the algorithm for these requirements, whilst decreasing the communication cost with a new data structure. The proposed algorithm converges to a conflict-free, feasible solution of which previous algorithms are unable to account for. Furthermore, the algorithm takes into account heterogeneous agents, deadlocking and a method to store assignments for a dynamical environment. Simulation results demonstrate reduced data usage and communication time to come to a consensus on multi-agent tasks.