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Urban commuters and road authorities are interested in knowing how well their city can manage traffic conditions. It is challenging to make a fair comparison among global cities due to the absence of a consistent, publicly accessible and inexpensive framework. This study proposes a methodology to compare travel time and average speed in different cities. Large cities have a central core, in which many of the major activities take place. This core is called downtown or the central business district (CBD). In this study, a comparison is made by simulating the morning commute trips from origins outside to destinations inside the CBD. The proposed framework outlines how the city and the CBD borders are defined. The data are collected by an accessible location service (Google Maps Distance Matrix Application Programming Interface). The framework is conducted on 11 major cities from the five continents, namely Cairo, London, Los Angeles, Melbourne, Moscow, New York, Paris, Singapore, Sydney, Tehran and Toronto. As such Singapore and Cairo had the shortest, while London and Paris had the longest travel times. According to the distribution graphs of average speed and travel time, Asian and African cities experienced a relatively higher average speed and a lower travel time.

Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions reduction, while routing the vehicles. It can be either through adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil fuel vehicles in fleets. GVRP also takes into account environmental sustainability in transportation and logistics. We critically review several variations and specializations of GVRP to address issues related to charging, pick-up, delivery, and energy consumption. Starting with the concepts and definitions of GVRP, we summarize the key elements and contributors to GVRP publications. Afterward, the issues regarding each category of green vehicle routing are reviewed, based on which key future research directions and challenges are suggested. It was observed that the main focus of previous publications is on the operational level routing decision and not the supply chain issues. The majority of publications used metaheuristic methods, while overlooking the emerging machine learning methods. We envision that in addition to machine learning, reinforcement learning, distributed systems, internet of vehicles (IoV), and new fuel technologies have a strong role in developing the GVRP research further.

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