Last-mile logistics optimization in the on-demand economy

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Amplifying Quiet Voices: Challenges and Opportunities for Participatory Design at an Urban Scale

Many Smart City projects are beginning to consider the role of citizens. However, current methods for engaging urban populations in participatory design activities are somewhat limited. In this paper, we describe an approach taken to empower socially disadvantaged citizens, using a variety of both social and technological tools, in a smart city project. Through analysing the nature of citizens’ concerns and proposed solutions, we explore the benefits of our approach, arguing that engaging citizens can uncover hyper-local concerns that provide a foundation for finding solutions to address citizen concerns. By reflecting on our approach, we identify four key challenges to utilising participatory design at an urban scale; balancing scale with the personal, who has control of the process, who is participating and integrating citizen-led work with local authorities. By addressing these challenges, we will be able to truly engage citizens as collaborators in co-designing their city

Governance in niche development for a transition to a new mobility regime

Urban mobility is a difficult sustainability challenge; measures to reduce transport impacts produce only marginal reductions in overall energy use and CO2 emissions. Even fuel switch to electric vehicles and measures to manage traffic produce insufficient improvements. Seeking transport sustainability within the existing socio-technical regime involves policy approaches for dense cities to provide high-capacity, corridor-based public transport, expecting people to arrange their lives around such transport systems. Yet this socio-technical regime ill-fits modern mobility needs. The reluctance to use public transport stems much from this 150 year old regime configuration. The social-technical landscape has shifted significantly: travel patterns are increasingly dispersed in space and time – not funnelled into traditional corridor peak-hour movements. The key is not getting people to return to travel patterns of 100 years ago, but in a transition to a socio-technical transport regime that delivers sustainability compatible with the 21st century social-technical landscape. An opportunity may be emerging for socio-technical configurations in niche environments to effect transitions to alternate mobility futures. Autonomous vehicles are rapidly approaching market application. Since 2011, small autonomous pods have operated on segregated tracks at Heathrow Airport. In 2014 a similar system opened at the Suncheon Bay tourist area in South Korea. Since 2011 there have been public street trials of autonomous vehicles in the USA and in 2015 they became street legal in the UK. The Milton Keynes (MK) ‘Pathfinder’ project focuses on two-seat pods which do not need segregated tracks, but will run on cycleways and footpaths, mixing with cyclists and pedestrians. Trials will start in 2015, on short distance links from the railway station to destinations in Central Milton Keynes. This project forms part of the wider Milton Keynes Future Cities Programme and Open University-led MK:Smart project. This paper draws on these trials in MK to show through case study research how autonomous vehicles applications are moving beyond protected niches and, along with other developments, hold the potential to stimulate a major transition in public transport systems. The vehicles are small and each journey is individual to the passenger(s). Services do not run along corridor routes, like buses and trams, but are based on alternate rule-sets to the existing regime with individual journeys customised for each user. Such developments may therefore stimulate transition to totally different sorts of public transport systems and ultimately, socio-technical mobility regimes, by offering much more to users than any corridor system can provide. Rather than people adjusting their behaviour to bus routes, schedules and operating times, they travel directly, whenever they want, on services running 24/7. Thus these new regimes could be more compatible with lifestyle and economic trends that comprise 21st century socio-technical landscapes. As such, they provide credible alternatives to the private car, and so hold potential to deliver major sustainability gains. But such transitions face major challenges from entrenched actors within the existing regime. Taxis, minicabs and bus operators would be threatened. If the Uber cab app is being blocked by incumbent actors, they look likely to be powerful opponents of autonomous vehicle based cab services. However, MK provides an interesting innovation context where there are several overlapping smart transport niches in different stages of development. As well as autonomous pods, demand responsive minibuses are planned and inductive changed electric buses are in service. If these projects build links to each other (niche accumulation), demonstrate economic value and reproduced beyond their original experimental spaces (niche proliferation), there is potential for them to overcome incumbent resistance. In Milton Keynes, these processes could be getting close to reaching critical mass, opening up the possibility of moving closer to radical regime transitions

Collaborative urban transportation : Recent advances in theory and practice

We thank the Leibniz Association for sponsoring the Dagstuhl Seminar 16091, at which the work presented here was initiated. We also thank Leena Suhl for her comments on an early version of this work. Finally, we thank the anonymous reviewers for the constructive comments.Peer reviewedPostprin

Optimization under Uncertainty for E-retail Distribution: From Suppliers to the Last Mile

This thesis examines problems faced in the distribution management of e-retailers, in different stages of the supply chain, while accounting for sources of uncertainty. The first problem studies distribution planning, under stochastic customer demand, in a transshipment network. To decide on a transportation schedule that minimizes transportation, inventory and outsourcing costs, the problem is formulated as a two-stage stochastic programming model with recourse. Computational experiments demonstrate the cost-effectiveness of distribution plans generated while considering uncertainty, and provide insights on conditions under which the proposed model achieves significant cost savings. We then focus our attention on a later phase in the supply chain: last-mile same-day delivery. We specifically study crowdsourced delivery, a new delivery system where freelance drivers deliver packages to customers with their own cars. We provide a comprehensive review of this system in terms of academic literature and industry practice. We present a classification of industry platforms based on their matching mechanisms, target markets, and compensation schemes. We also identify new challenges that this delivery system brings about, and highlight open research questions. We then investigate two important research questions faced by crowdsourced delivery platforms. The second problem in this thesis examines the question of balancing driver capacity and demand in crowdsourced delivery systems when there is randomness in supply and demand. We propose models and test the use of heatmaps as a balancing tool for directing drivers to regions with shortage, with an increased likelihood, but not a guarantee, of a revenue-producing order match. We develop an MDP model to sequentially select matching and heatmap decisions that maximize demand fulfillment. The model is solved using a stochastic look-ahead policy, based on approximate dynamic programming. Computational experiments on a real-world dataset demonstrate the value of heatmaps, and factors that impact the effectiveness of heatmaps in improving demand fulfillment. The third problem studies the integration of driver welfare considerations within a platform's dynamic matching decisions. This addresses the common criticism of the lack of protection for workers in the sharing economy, by proposing compensation guarantees to drivers, while maintaining the work hour flexibility of the sharing economy. We propose and model three types of compensation guarantees, either utilization-based or wage-based. We formulate an MDP model, then utilize value function approximation to efficiently solve the problem. Computational experiments are presented to assess the proposed solution approach and evaluate the impact of the different types of guarantees on both the platform and the drivers

An investigation into the role of crowdsourcing in generating information for flood risk management

Flooding is a major global hazard whose management relies on an accurate understanding of its risks. Crowdsourcing represents a major opportunity for supporting flood risk management as members of the public are highly capable of producing useful flood information. This thesis explores a wide range of issues related to flood crowdsourcing using an interdisciplinary approach. Through an examination of 31 different projects a flood crowdsourcing typology was developed. This identified five key types of flood crowdsourcing: i) Incident Reporting, ii) Media Engagement, iii) Collaborative Mapping, iv) Online Volunteering and v) Passive VGI. These represent a wide range of initiatives with radically different aims, objectives, datasets and relationships with volunteers. Online Volunteering was explored in greater detail using Tomnod as a case study. This is a micro-tasking platform in which volunteers analyse satellite imagery to support disaster response. Volunteer motivations for participating on Tomnod were found to be largely altruistic. Demographics of participants were significant, with retirement, disability or long-term health problems identified as major drivers for participation. Many participants emphasised that effective communication between volunteers and the site owner is strongly linked to their appreciation of the platform. In addition, the feedback on the quality and impact of their contributions was found to be crucial in maintaining interest. Through an examination of their contributions, volunteers were found to be able to ascertain with a higher degree of accuracy, many features in satellite imagery which supervised image classification struggled to identify. This was more pronounced in poorer quality imagery where image classification had a very low accuracy. However, supervised classification was found to be far more systematic and succeeded in identifying impacts in many regions which were missed by volunteers. The efficacy of using crowdsourcing for flood risk management was explored further through the iterative development of a Collaborative Mapping web-platform called Floodcrowd. Through interviews and focus groups, stakeholders from the public and private sector expressed an interest in crowdsourcing as a tool for supporting flood risk management. Types of data which stakeholders are particularly interested in with regards to crowdsourcing differ between organisations. Yet, they typically include flood depths, photos, timeframes of events and historical background information. Through engagement activities, many citizens were found to be able and motivated to share such observations. Yet, motivations were strongly affected by the level of attention their contributions receive from authorities. This presents many opportunities as well as challenges for ensuring that the future of flood crowdsourcing improves flood risk management and does not damage stakeholder relationships with participants