Concept of the knowledge-based city logisticsProblems and solutions

Stanisław Iwan, Natalia Wagner, Kinga Kijewska, Sidsel Ahlmann Jensen•View original
HighlightsTechnicalcoral voice
Cities are racing to become smart by filling streets with sensors and deploying emerging technologies. Yet, urban freight efficiency keeps lagging. Iwan, Wagner, Kijewska, and Jensen ask the hard question directly: is implementing technological tools actually sufficient to solve the problems of intensely growing urban freight transport? Their answer, grounded in a survey of thirty-one international city logistics specialists, is no. The bottleneck isn't hardware; it's knowledge. To make that argument precise, the team adapts the Wisdom-Knowledge-Information-Data pyramid to city logistics. Raw data like commercial vehicle counts, road accident rates, and infrastructure parameters become information when given context around congestion and stakeholder needs. They then become knowledge about last-mile delivery processes, and finally, wisdom that enables sustainable urban logistics planning. Four knowledge management processes map onto that hierarchy: acquisition planning, data gathering and knowledge acquisition, knowledge creation and application, and knowledge transfer. The whole ecosystem of municipalities, retailers, and logistics operators is supposed to run this cycle continuously. In practice, it breaks down. The survey, fielded between March and July 2021 using a computer-assisted web interview method, asked experts how frequently a knowledge-based approach is applied across six city logistics activities. Mapping stakeholder needs scored highest, averaging three point seventy-four on a five-point scale, with a median of four. Decisions on new city infrastructure like consolidation centers and loading bays scored lowest, at three point nineteen. When experts identified where the knowledge management process actually fails, data gathering and knowledge acquisition came out on top. The core reason: stakeholder unwillingness. The reluctance of city users, retailers, and transport operators to share information rated three point ninety-four out of five. "Necessary data are not gathered or stored" rated three point seventy-seven. Insufficient funds for data gathering came in at three point fifty-five. Notably, not a single expert answered "never" to experiencing the willingness problem. Data on demand for goods delivery were flagged as both the most needed and among the hardest to obtain. The proposed solution is a two-part collaborative knowledge base. The first component is a City Logistics Knowledge Management platform — an online repository housing market reports, best-practice documents, datasets, and tools like a carbon-footprint calculator, built city by city and designed for cross-city benchmarking. The second is the Freight Quality Partnership, involving regular multi-stakeholder meetings to surface tacit knowledge, consult public decision-making, and translate stakeholder requirements into regulations. Together, these two pillars directly target what the survey identified — the combination of technical infrastructure and enforced dialogue addresses both the data gaps and the organizational reluctance. Looking ahead, the authors also flag drone-mounted cameras as a way to collect freight data without depending on road users at all. The practical implication is straightforward. Researchers embedded in city logistics projects act as evangelists and early adopters, diffusing knowledge from pilot cities to followers. Municipalities and logistics managers should engage those actors deliberately. The sensors matter, but only once the organizational will to share what they capture is in place.

Cities are racing to become smart by filling streets with sensors and deploying emerging technologies. Yet, urban freight efficiency keeps lagging. Iwan, Wagner, Kijewska, and Jensen ask the hard question directly: is implementing technological tools actually sufficient to solve the problems of intensely growing urban freight transport?

Their answer, grounded in a survey of thirty-one international city logistics specialists, is no. The bottleneck isn't hardware; it's knowledge.

To make that argument precise, the team adapts the Wisdom-Knowledge-Information-Data pyramid to city logistics. Raw data like commercial vehicle counts, road accident rates, and infrastructure parameters become information when given context around congestion and stakeholder needs. They then become knowledge about last-mile delivery processes, and finally, wisdom that enables sustainable urban logistics planning.

Four knowledge management processes map onto that hierarchy: acquisition planning, data gathering and knowledge acquisition, knowledge creation and application, and knowledge transfer. The whole ecosystem of municipalities, retailers, and logistics operators is supposed to run this cycle continuously. In practice, it breaks down.

The survey, fielded between March and July 2021 using a computer-assisted web interview method, asked experts how frequently a knowledge-based approach is applied across six city logistics activities. Mapping stakeholder needs scored highest, averaging three point seventy-four on a five-point scale, with a median of four. Decisions on new city infrastructure like consolidation centers and loading bays scored lowest, at three point nineteen.

When experts identified where the knowledge management process actually fails, data gathering and knowledge acquisition came out on top. The core reason: stakeholder unwillingness. The reluctance of city users, retailers, and transport operators to share information rated three point ninety-four out of five. "Necessary data are not gathered or stored" rated three point seventy-seven.

Insufficient funds for data gathering came in at three point fifty-five. Notably, not a single expert answered "never" to experiencing the willingness problem. Data on demand for goods delivery were flagged as both the most needed and among the hardest to obtain.

The proposed solution is a two-part collaborative knowledge base. The first component is a City Logistics Knowledge Management platform — an online repository housing market reports, best-practice documents, datasets, and tools like a carbon-footprint calculator, built city by city and designed for cross-city benchmarking. The second is the Freight Quality Partnership, involving regular multi-stakeholder meetings to surface tacit knowledge, consult public decision-making, and translate stakeholder requirements into regulations.

Together, these two pillars directly target what the survey identified — the combination of technical infrastructure and enforced dialogue addresses both the data gaps and the organizational reluctance. Looking ahead, the authors also flag drone-mounted cameras as a way to collect freight data without depending on road users at all.

The practical implication is straightforward. Researchers embedded in city logistics projects act as evangelists and early adopters, diffusing knowledge from pilot cities to followers. Municipalities and logistics managers should engage those actors deliberately.

The sensors matter, but only once the organizational will to share what they capture is in place.